@article {719886,
	title = {Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life: an application to settings in Europe, Asia, Africa, and South America},
	journal = {medRxiv preprint doi: ttps://doi.org/10.1101/2024.02.21.24303099},
	year = {2024},
	abstract = {Long-term COVID-19 complications are a globally pervasive threat, but their plausible social drivers are often not prioritized. Here, we use data from a multinational consortium to quantify the relative contributions of social and clinical factors to differences in quality of life among participants experiencing long COVID and measure the extent to which social variables{\textquoteright} impacts can be attributed to clinical intermediates, across diverse contexts. In addition to age, neuropsychological and rheumatological comorbidities, educational attainment, employment status, and female sex were identified as important predictors of long COVID-associated quality of life days (long COVID QALDs). Furthermore, a great majority of their impacts on long COVID QALDs could not be tied to key long COVID-predicting comorbidities, such as asthma, diabetes, hypertension, psychological disorder, and obesity. In Norway, 90\% (95\% CI: 77\%, 100\%) of the effect of belonging to the highest versus lowest educational attainment quintile was not attributed to intermediate comorbidity impacts. The same was true for 86\% (73\%, 100\%) of the protective effects of full-time employment versus all other employment status categories (excluding retirement) in the UK and 74\% (46\%,100\%) of the protective effects of full-time employment versus all other employment status categories in a cohort of four middle-income countries (MIC). Of the effects of female sex on long COVID QALDs in Norway, UK, and the MIC cohort, 77\% (46\%,100\%), 73\% (52\%, 94\%), and 84\% (62\%, 100\%) were unexplained by the clinical mediators, respectively. Our findings highlight that socio-economic proxies and sex may be as predictive of long COVID QALDs as commonly emphasized comorbidities and that broader structural determinants likely drive their impacts. Importantly, we outline a multi-method, adaptable causal machine learning approach for evaluating the isolated contributions of social disparities to long COVID quality of life experiences.},
	url = {https://www.medrxiv.org/content/10.1101/2024.02.21.24303099v1},
	author = {Tigist F. Menkir and Citarella, Barbara Wanjiru and Sigfrid, Louise and Yash Doshi and Reyes, Luis Felipe and Jose A. Calvache and Anders Benjamin Kildal and Anders B. Nygaard and Jan Cato Holter and Panda, Prasan Kumar and Jassat, Waasila and Merson, Laura and Christl A. Donnelly and Santillana, Mauricio and Buckee, Caroline and Verguet, St{\'e}phane and Nima S. Hejazi}
}
@article {719336,
	title = {Cognitive Symptoms of Post{\textendash}COVID-19 Condition and Daily Functioning},
	journal = {JAMA Netw },
	year = {2024},
	abstract = {Importance\&nbsp;\&nbsp;The frequent occurrence of cognitive symptoms in post{\textendash}COVID-19 condition has been described, but the nature of these symptoms and their demographic and functional factors are not well characterized in generalizable populations.Objective\&nbsp;\&nbsp;To investigate the prevalence of self-reported cognitive symptoms in post{\textendash}COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post{\textendash}COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status.Design, Setting, and Participants\&nbsp;\&nbsp;Two waves of a 50-state nonprobability population-based internet survey conducted between December 22, 2022, and May 5, 2023. Participants included survey respondents aged 18 years and older.Exposure\&nbsp;\&nbsp;Post{\textendash}COVID-19 condition, defined as self-report of symptoms attributed to COVID-19 beyond 2 months after the initial month of illness.Main Outcomes and Measures\&nbsp;\&nbsp;Seven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9.Results\&nbsp;\&nbsp;The 14 767 individuals reporting test-confirmed COVID-19 illness at least 2 months before the survey had a mean (SD) age of 44.6 (16.3) years; 568 (3.8\%) were Asian, 1484 (10.0\%) were Black, 1408 (9.5\%) were Hispanic, and 10 811 (73.2\%) were White. A total of 10 037 respondents (68.0\%) were women and 4730 (32.0\%) were men. Of the 1683 individuals reporting post{\textendash}COVID-19 condition, 955 (56.7\%) reported at least 1 cognitive symptom experienced daily, compared with 3552 of 13 084 (27.1\%) of those who did not report post{\textendash}COVID-19 condition. More daily cognitive symptoms were associated with a greater likelihood of reporting at least moderate interference with functioning (unadjusted odds ratio [OR], 1.31 [95\% CI, 1.25-1.36]; adjusted [AOR], 1.30 [95\% CI, 1.25-1.36]), lesser likelihood of full-time employment (unadjusted OR, 0.95 [95\% CI, 0.91-0.99]; AOR, 0.92 [95\% CI, 0.88-0.96]) and greater severity of depressive symptoms (unadjusted coefficient, 1.40 [95\% CI, 1.29-1.51]; adjusted coefficient 1.27 [95\% CI, 1.17-1.38). After including depressive symptoms in regression models, associations were also found between cognitive symptoms and at least moderate interference with everyday functioning (AOR, 1.27 [95\% CI, 1.21-1.33]) and between cognitive symptoms and lower odds of full-time employment (AOR, 0.92 [95\% CI, 0.88-0.97]).​Conclusions and Relevance\&nbsp;\&nbsp;The findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post{\textendash}COVID-19 condition and associated with greater self-reported functional impairment, lesser likelihood of full-time employment, and greater depressive symptom severity. Screening for and addressing cognitive symptoms is an important component of the public health response to post{\textendash}COVID-19 condition.},
	url = {https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2815067?utm_campaign=articlePDF\&utm_medium=articlePDFlink\&utm_source=articlePDF\&utm_content=jamanetworkopen.2023.56098},
	author = {Abhishek Jaywant and Faith M. Gunning and Lauren E. Oberlin and Santillana, Mauricio and Katherine Ognyanova and ames N. Druckman and Baum, Matthew A. and Lazer, David and Roy H. Perlis}
}
@article {719331,
	title = {Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics},
	journal = {medRxiv},
	year = {2024},
	abstract = {
	The COVID-19 pandemic has not only presented a major global public health and socio-economic crisis, but has also significantly impacted human behavior towards adherence (or lack thereof) to public health intervention and mitigation measures implemented in communities worldwide. The dynamic nature of the pandemic has prompted extensive changes in individual and collective behaviors towards the pandemic. This study is based on the use of mathematical modeling approaches to assess the extent to which SARS-CoV-2 transmission dynamics is impacted by population-level changes of human behavior due to factors such as (a) the severity of transmission (such as disease-induced mortality and level of symptomatic transmission), (b) fatigue due to the implementation of mitigation interventions measures (e.g., lockdowns) over a long (extended) period of time, (c) social peer-pressure, among others. A novel behavior-epidemiology model, which takes the form of a deterministic system of nonlinear differential equations, is developed and fitted using observed cumulative SARS-CoV-2 mortality data during the first wave in the United States. Rigorous analysis of the model shows that its disease-free equilibrium is locally-asymptotically stable whenever a certain epidemiological threshold, known as the\&nbsp;control reproduction number\&nbsp;(denoted by\&nbsp;RC\&nbsp;) is less than one, and the disease persists (i.e., causes significant outbreak or outbreaks) if the threshold exceeds one. The model fits the observed data, as well as makes a more accurate prediction of the observed daily SARS-CoV-2 mortality during the first wave (March 2020 -June 2020), in comparison to the equivalent model which does not explicitly account for changes in human behavior. Of the various metrics for human behavior changes during the pandemic considered in this study, it is shown that behavior changes due to the level of SARS-CoV-2 mortality and symptomatic transmission were more influential (while behavioral changes due to the level of fatigue to interventions in the community was of marginal impact). It is shown that an increase in the proportion of exposed individuals who become asymptomatically-infectious at the end of the exposed period (represented by a parameter\&nbsp;r) can lead to an increase (decrease) in the control reproduction number (RC) if the effective contact rate of asymptomatic individuals is higher (lower) than that of symptomatic individuals. The study identifies two threshold values of the parameter\&nbsp;r\&nbsp;that maximize the cumulative and daily SARS-CoV-2 mortality, respectively, during the first wave. Furthermore, it is shown that, as the value of the proportion\&nbsp;r\&nbsp;increases from 0 to 1, the rate at which susceptible non-adherent individuals change their behavior to strictly adhere to public health interventions decreases. Hence, this study suggests that, as more newly- infected individuals become asymptomatically-infectious, the level of positive behavior change, as well as disease severity, hospitalizations and disease-induced mortality in the community can be expected to significantly decrease (while new cases may rise, particularly if asymptomatic individuals have higher contact rate, in comparison to symptomatic individuals).

},
	url = {https://www.medrxiv.org/content/10.1101/2024.02.11.24302662v1.full.pdf},
	author = {Binod Pant and Salman Safdar and Santillana, Mauricio and Abba B. Gumel}
}
@article {716941,
	title = {Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States},
	journal = {medRxiv},
	year = {2024},
	abstract = {The coronavirus (COVID-19) pandemic has profoundly impacted various aspects of daily life, society, healthcare systems, and global health policies. This pandemic has resulted in more than one hundred million people being infected and, unfortunately, the loss of life for many individuals. Although treatment for the coronavirus is now available, effective forecasting of COVID-19 infec- tion is the most importance to aid public health officials in making critical decisions. However, forecasting COVID-19 trends through time-series analysis poses significant challenges due to the data{\textquoteright}s inherently dynamic, transient, and noise-prone nature. In this study, we have developed the Fine-Grained Infection Forecast Network (FIGI-Net) model, which provides accurate forecasts of COVID-19 trends up to two weeks in advance. FIGI-Net addresses the current limitations in COVID-19 forecasting by leveraging fine-grained county-level data and a stacked bidirectional LSTM structure. We employ a pre-trained model to capture essential global infection patterns. Subsequently, these pre-trained parameters were transferred to train localized sub-models for county clusters exhibiting comparable infection dynamics. This model adeptly handles sudden changes and rapid fluctuations in data, frequently observed across various times and locations of county-level data, ultimately improving the accuracy of COVID-19 infection forecasting at the county, state, and national levels. FIGI-Net model demonstrated significant improvement over other deep learning-based models and state-of-the-art COVID-19 forecasting models, evident in various standard evaluation metrics. Notably, FIGI-Net model excels at forecasting the direction of infection trends, especially during the initial phases of different COVID-19 outbreak waves. Our study underscores the effectiveness and superiority of our time-series deep learning-based methods in addressing dynamic and sudden changes in infection numbers over short-term time periods. These capabilities facilitate efficient public health management and the early implementation of COVID-19 transmission prevention measures.},
	url = {https://www.medrxiv.org/content/10.1101/2024.01.13.24301248v1},
	author = {Tzu-Hsi Songa and Clemente, Leonardo and Xiang Pana and Junbong Janga and Mauricio Santillanab and Kwonmoo Leea}
}
@article {716936,
	title = {Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study},
	journal = {Journal of Medical Internet Research},
	volume = {26},
	year = {2024},
	abstract = {Background:\&nbsp;The correlates responsible for the temporal changes of intrahousehold SARS-CoV-2 transmission in the United States have been understudied mainly due to a lack of available surveillance data. Specifically, early analyses of SARS-CoV-2 household secondary attack rates (SARs) were small in sample size and conducted cross-sectionally at single time points. From these limited data, it has been difficult to assess the role that different risk factors have had on intrahousehold disease transmission in different stages of the ongoing COVID-19 pandemic, particularly in children and youth.Objective:\&nbsp;This study aimed to estimate the transmission dynamic and infectivity of SARS-CoV-2 among pediatric and young adult index cases (age 0 to 25 years) in the United States through the initial waves of the pandemic.Methods:\&nbsp;Using administrative claims, we analyzed 19 million SARS-CoV-2 test records between January 2020 and February 2021. We identified 36,241 households with pediatric index cases and calculated household SARs utilizing complete case information. Using a retrospective cohort design, we estimated the household SARS-CoV-2 transmission between 4 index age groups (0 to 4 years, 5 to 11 years, 12 to 17 years, and 18 to 25 years) while adjusting for sex, family size, quarter of first SARS-CoV-2 positive record, and residential regions of the index cases.Results:\&nbsp;After filtering all household records for greater than one member in a household and missing information, only 36,241 (0.85\%) of 4,270,130 households with a pediatric case remained in the analysis. Index cases aged between 0 and 17 years were a minority of the total index cases (n=11,484, 11\%). The overall SAR of SARS-CoV-2 was 23.04\% (95\% CI 21.88-24.19). As a comparison, the SAR for all ages (0 to 65+ years) was 32.4\% (95\% CI 32.1-32.8), higher than the SAR for the population between 0 and 25 years of age. The highest SAR of 38.3\% was observed in April 2020 (95\% CI 31.6-45), while the lowest SAR of 15.6\% was observed in September 2020 (95\% CI 13.9-17.3). It consistently decreased from 32\% to 21.1\% as the age of index groups increased. In a multiple logistic regression analysis, we found that the youngest pediatric age group (0 to 4 years) had 1.69 times (95\% CI 1.42-2.00) the odds of SARS-CoV-2 transmission to any family members when compared with the oldest group (18 to 25 years). Family size was significantly associated with household viral transmission (odds ratio 2.66, 95\% CI 2.58-2.74).​Conclusions:\&nbsp;Using retrospective claims data, the pediatric index transmission of SARS-CoV-2 during the initial waves of the COVID-19 pandemic in the United States was associated with location and family characteristics. Pediatric SAR (0 to 25 years) was less than the SAR for all age other groups. Less than 1\% (n=36,241) of all household data were retained in the retrospective study for complete case analysis, perhaps biasing our findings. We have provided measures of baseline household pediatric transmission for tracking and comparing the infectivity of later SARS-CoV-2 variants.},
	url = {https://www.jmir.org/2024/1/e44249},
	author = {Ming Kei Chung and Brian Hart and Santillana, Mauricio and Patel, Chirag J}
}
@article {715356,
	title = {Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations},
	journal = {medRxiv preprint doi: https://doi.org/10.1101/2023.12.08.23299726},
	year = {2023},
	abstract = {Accurate forecasts can enable more effective public health responses during seasonal influenza epidemics. Forecasting teams were asked to provide national and jurisdiction-specific probabilistic predictions of weekly confirmed influenza hospital admissions for one through four weeks ahead for the 2021-22 and 2022-23 influenza seasons.Across both seasons, 26 teams submitted forecasts, with the submitting teams varying between seasons. Forecast skill was evaluated using the Weighted Interval Score (WIS), relative WIS, and coverage.Six out of 23 models outperformed the baseline model across forecast weeks and locations in 2021-22 and 12 out of 18 models in 2022-23. Averaging across all forecast targets, the FluSight ensemble was the 2nd\&nbsp;most accurate model measured by WIS in 2021-22 and the 5th\&nbsp;most accurate in the 2022-23 season. Forecast skill and 95\% coverage for the FluSight ensemble and most component models degraded over longer forecast horizons and during periods of rapid change.Current influenza forecasting efforts help inform situational awareness, but research is needed to address limitations, including decreased performance during periods of changing epidemic dynamics.},
	url = {https://www.medrxiv.org/content/10.1101/2023.12.08.23299726v1},
	author = {Sarabeth M. Mathis and Alexander E. Webber and Tom{\'a}s M. Le{\'o}n and Erin L. Murray and Monica Sun and Lauren A. White and Logan C. Brooks and Alden Green and Addison J. Hu and Daniel J. McDonald and Roni Rosenfeld and Dmitry Shemetov and Ryan J. Tibshirani and Sasikiran Kandula and Sen Pei and effrey Shaman and Rami Yaari and Teresa K. Yamana and B. Aditya Prakash and Rishi Raman and Alexander Rodr{\'\i}guez and Zhiyuan Zhao and Akilan Meiyappan and Shalina Omar and Prasith Baccam and Heidi L. Gurung and Steve A. Stage and Brad T. Suchoski and Marco Ajelli and Allisandra G. Kummer and Maria Litvinova and Paulo C. Ventura and Spencer Wadsworth and Jarad Niemi and Erica Carcelen and Alison L. Hill and Sung-mok Jung and oseph C. Lemaitre and Lessler, Justin and Sara L Loo and Clifton D. McKee and Koji Sato and Claire Smith and Shaun Truelove and Thomas McAndrew and Wenxuan Ye and Nikos Bosse and William S. Hlavacek and Lin, Yen Ting and Abhishek Mallela and Ye Chen and Shelby M. Lamm and Jaechoul Lee and Richard G. Posner and Amanda C. Perofsky and C{\'e}cile Viboud and Clemente, Leonardo and Lu, Fred and Autin G. Meyer and Santillana, Mauricio and Matteo Chinazzi and Jessica T. Davis and Kunpeng Mu and Ana Pastore y Piontti and Alessandro Vespignani and Xinyue Xiong and Michal Ben-Nun and Pete Riley and James Turtle and Chis Hulme-Lowe and Shakeel Jessa and V.P. Nagraj and Stephen D. Turner and Desiree Williams and Avranil Basu and John M. Drake and Spencer J. Fox and Graham C. Gibson and Ehsan Suez and Edward W. Thommes and Monica G. Cojocaru and Estes Y. Cramer and Aaron Gerding and Ariane Stark and Evan L. Ray and Nicholas G. Reich and Li Shandross and Nutcha Wattanachit and Yijin Wang and Martha W. Zorn and Majd Al Aawar and Ajitesh Srivastava and Lauren A. Meyers and Aniruddha Adiga and Benjamin Hurt and Gursharn Kaur and Bryan L. Lewis and Madhav Marathe and Srinivasan Venkatramanan and Patrick Butler and Andrew Farabow and Nikhil Muralidhar and Naren Ramakrishnan and Carrie Reed and Matthew Biggerstaff and Rebecca K. Borchering}
}
@article {715346,
	title = {De-identification and Obfuscation of Gender Attributes from Retinal Scans},
	journal = {Clinical Image-Based Procedures, Fairness of AI in Medical Imaging, and Ethical and Philosophical Issues in Medical Imaging},
	volume = {14242},
	year = {2023},
	pages = {91-101},
	abstract = {Retina images are considered to be important biomarkers and have been used as clinical diagnostic tools to detect multiple diseases. We examine multiple techniques for de-identifying retina images while maintaining their clinical ability for detecting diabetic retinopathy (DR), using gender as a proxy for identifiability. We apply two differential privacy algorithms, Snow and VS-Snow, on the entire image (globally) and on blood vessels only (locally) to obfuscate important image features that can predict a patient{\textquoteright}s sex. We evaluate the level of privacy and retained clinical predictive power of these de-identified images by using attacking gender classifier models and downstream disease classifiers. We show empirically that our proposed VS-Snow framework achieves strong privacy while preserving a meaningful clinical predictive power across different patient populations.},
	url = {https://link.springer.com/chapter/10.1007/978-3-031-45249-9_9},
	author = {Chenwei Wu and Xiyu Yang and Emil Ghitman Gilkes and Hanwen Cui and Jiheon Choi and Na Sun and Ziqian Liao and Bo Fan and Santillana, Mauricio and Celi, Leo and Paolo Silva and Luis Nakayama}
}
@article {710896,
	title = {Misinformation, Trust, and Use of Ivermectin and Hydroxychloroquine for COVID-19},
	journal = {JAMA Health Forum},
	year = {2023},
	abstract = {Importance\&nbsp;\&nbsp;The COVID-19 pandemic has been notable for the widespread dissemination of misinformation regarding the virus and appropriate treatment.Objective\&nbsp;\&nbsp;To quantify the prevalence of non{\textendash}evidence-based treatment for COVID-19 in the US and the association between such treatment and endorsement of misinformation as well as lack of trust in physicians and scientists.Design, Setting, and Participants\&nbsp;\&nbsp;This single-wave, population-based, nonprobability internet survey study was conducted between December 22, 2022, and January 16, 2023, in US residents 18 years or older who reported prior COVID-19 infection.Main Outcome and Measure\&nbsp;\&nbsp;Self-reported use of ivermectin or hydroxychloroquine, endorsing false statements related to COVID-19 vaccination, self-reported trust in various institutions, conspiratorial thinking measured by the American Conspiracy Thinking Scale, and news sources.Results\&nbsp;\&nbsp;A total of 13 438 individuals (mean [SD] age, 42.7 [16.1] years; 9150 [68.1\%] female and 4288 [31.9\%] male) who reported prior COVID-19 infection were included in this study. In this cohort, 799 (5.9\%) reported prior use of hydroxychloroquine (527 [3.9\%]) or ivermectin (440 [3.3\%]). In regression models including sociodemographic features as well as political affiliation, those who endorsed at least 1 item of COVID-19 vaccine misinformation were more likely to receive non{\textendash}evidence-based medication (adjusted odds ratio [OR], 2.86; 95\% CI, 2.28-3.58). Those reporting trust in physicians and hospitals (adjusted OR, 0.74; 95\% CI, 0.56-0.98) and in scientists (adjusted OR, 0.63; 95\% CI, 0.51-0.79) were less likely to receive non{\textendash}evidence-based medication. Respondents reporting trust in social media (adjusted OR, 2.39; 95\% CI, 2.00-2.87) and in Donald Trump (adjusted OR, 2.97; 95\% CI, 2.34-3.78) were more likely to have taken non{\textendash}evidence-based medication. Individuals with greater scores on the American Conspiracy Thinking Scale were more likely to have received non{\textendash}evidence-based medications (unadjusted OR, 1.09; 95\% CI, 1.06-1.11; adjusted OR, 1.10; 95\% CI, 1.07-1.13).​Conclusions and Relevance\&nbsp;\&nbsp;In this survey study of US adults, endorsement of misinformation about the COVID-19 pandemic, lack of trust in physicians or scientists, conspiracy-mindedness, and the nature of news sources were associated with receiving non{\textendash}evidence-based treatment for COVID-19. These results suggest that the potential harms of misinformation may extend to the use of ineffective and potentially toxic treatments in addition to avoidance of health-promoting behaviors.},
	url = {https://jamanetwork.com/journals/jama-health-forum/fullarticle/2809985},
	author = {Roy H. Perlis and Trujillo, Kristin Lunz and Jon Green and Safarpour, Alauna and James N. Druckman and Santillana, Mauricio and Katherine Ognyanova and Lazer, David}
}
@article {710886,
	title = {Community Mobility and Depressive Symptoms During the COVID-19 Pandemic in the United States},
	journal = {JAMA Network Open},
	year = {2023},
	abstract = {Importance\&nbsp;\&nbsp;Marked elevation in levels of depressive symptoms compared with historical norms have been described during the COVID-19 pandemic, and understanding the extent to which these are associated with diminished in-person social interaction could inform public health planning for future pandemics or other disasters.Objective\&nbsp;\&nbsp;To describe the association between living in a US county with diminished mobility during the COVID-19 pandemic and self-reported depressive symptoms, while accounting for potential local and state-level confounding factors.Design, Setting, and Participants\&nbsp;\&nbsp;This survey study used 18 waves of a nonprobability internet survey conducted in the United States between May 2020 and April 2022. Participants included respondents who were 18 years and older and lived in 1 of the 50 US states or Washington DC.Main Outcome and Measure\&nbsp;\&nbsp;Depressive symptoms measured by the Patient Health Questionnaire-9 (PHQ-9); county-level community mobility estimates from mobile apps; COVID-19 policies at the US state level from the Oxford stringency index.Results\&nbsp;\&nbsp;The 192 271 survey respondents had a mean (SD) of age 43.1 (16.5) years, and 768 (0.4\%) were American Indian or Alaska Native individuals, 11 448 (6.0\%) were Asian individuals, 20 277 (10.5\%) were Black individuals, 15 036 (7.8\%) were Hispanic individuals, 1975 (1.0\%) were Pacific Islander individuals, 138 702 (72.1\%) were White individuals, and 4065 (2.1\%) were individuals of another race. Additionally, 126 381 respondents (65.7\%) identified as female and 65 890 (34.3\%) as male. Mean (SD) depression severity by PHQ-9 was 7.2 (6.8). In a mixed-effects linear regression model, the mean county-level proportion of individuals not leaving home was associated with a greater level of depression symptoms (β, 2.58; 95\% CI, 1.57-3.58) after adjustment for individual sociodemographic features. Results were similar after the inclusion in regression models of local COVID-19 activity, weather, and county-level economic features, and persisted after widespread availability of COVID-19 vaccination. They were attenuated by the inclusion of state-level pandemic restrictions. Two restrictions, mandatory mask-wearing in public (β, 0.23; 95\% CI, 0.15-0.30) and policies cancelling public events (β, 0.37; 95\% CI, 0.22-0.51), demonstrated modest independent associations with depressive symptom severity.​Conclusions and Relevance\&nbsp;\&nbsp;In this study, depressive symptoms were greater in locales and times with diminished community mobility. Strategies to understand the potential public health consequences of pandemic responses are needed.},
	url = {https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2809947?resultClick=3},
	author = {Roy H. Perlis and Trujillo, Kristin Lunz and Safarpour, Alauna and Alexi Quintana and Simonson, Matthew D. and Jasper Perlis and Santillana, Mauricio and Katherine Ognyanova and Baum, Matthew A. and James N. Druckman and Lazer, David}
}
@article {710876,
	title = {Predicting favorable response to intravenous morphine in pediatric critically ill cardiac patients},
	journal = {Pharmacotherapy},
	year = {2023},
	abstract = {Introduction:\&nbsp;Analgesia and sedation are integral to the care of critically ill children. However, the choice and dose of the analgesic or sedative drug is often empiric, and models predicting favorable responses are lacking. We aimed to compute models to predict a patient{\textquoteright}s response to intravenous morphine.Methods:\&nbsp;We retrospectively analyzed data from consecutive patients admitted to the Cardiac Intensive Care Unit (January 2011{\textendash}January 2020) who received at least one intravenous bolus of morphine. The primary outcome was a decrease in the State Behavioral Scale (SBS) >=1 point; the secondary outcome was a decrease in the heart rate Z-score (zHR) at 30 min. Effective doses were modeled using logistic regression, Lasso regression, and random forest modeling.Results:\&nbsp;A total of 117,495 administrations of intravenous morphine among 8140 patients (median age 0.6 years [interquartile range [IQR] 0.19, 3.3]) were included. The median morphine dose was 0.051 mg/kg (IQR 0.048, 0.099) and the median 30- day cumulative dose was 2.2 mg/kg (IQR 0.4, 15.3). SBS decreased following 30\% of doses, did not change following 45\%, and increased following 25\%. The zHR signifi- cantly decreased after morphine administration (median delta-zHR -0.34 [IQR-1.03, 0.00],\&nbsp;p\&nbsp;\&lt;\&nbsp;0.001). The following factors were associated with favorable response to morphine: A concomitant infusion of propofol, higher prior 30-day cumulative dose, being invasively ventilated and/or on vasopressors. Higher morphine dose, higher zHR pre-morphine, an additional analgosedation bolus\&nbsp;{\textpm}30 min around the index bolus, a concomitant ketamine or dexmedetomidine infusion, and showing signs of withdrawal syndrome were associated with unfavorable response. Logistic regression (area under the receiver operating characteristic [ROC] curve [AUC] 0.900) and machine learning models (AUC 0.906) performed comparably, with a sensitivity of 95\%, specificity of 71\%, and negative predictive value of 97\%.Conclusions:\&nbsp;Statistical models identify 95\% of effective intravenous morphine doses in pediatric critically ill cardiac patients, while incorrectly suggesting an effective dose in 29\% of cases. This work represents an important step toward computer-aided, per- sonalized clinical decision support tool for sedation and analgesia in ICU patients.},
	url = {https://accpjournals.onlinelibrary.wiley.com/doi/10.1002/phar.2835},
	author = {Francesca Sperotto and Siva Emani and Lin Zhu and Marl{\`o}n Delgado and Santillana, Mauricio and John N. Kheir}
}
@article {710871,
	title = {Using digital traces to build prospective and real-timecounty-level early warning systems to anticipate COVID-19 outbreaks in the United States},
	journal = {Science Advances},
	volume = {9},
	number = {3},
	year = {2023},
	abstract = {Coronavirus disease 2019 (COVID-19) continues to affect the world, and the design of strategies to curb disease outbreaks requires close monitoring of their trajectories. We present machine learning methods that leverage internet-based digital traces to anticipate sharp increases in COVID-19 activity in U.S. counties. In a complemen- tary direction to the efforts led by the Centers for Disease Control and Prevention (CDC), our models are de- signed to detect the time when an uptrend in COVID-19 activity will occur. Motivated by the need for finer spatial resolution epidemiological insights, we build upon previous efforts conceived at the state level. Our methods{\textemdash}tested in an out-of-sample manner, as events were unfolding, in 97 counties representative of mul- tiple population sizes across the United States{\textemdash}frequently anticipated increases in COVID-19 activity 1 to 6 weeks before local outbreaks, defined when the effective reproduction number\&nbsp;Rt\&nbsp;becomes larger than 1 for a period of 2 weeks.​},
	url = {https://www.science.org/doi/epdf/10.1126/sciadv.abq0199},
	author = {Lucas M. Stolerman and Clemente, Leonardo and Canelle Poirier and Kris V. Parag and Atreyee Majumder and Serge Masyn and Bernd Resch and Santillana, Mauricio}
}
@article {710866,
	title = {A 50-state survey study of thoughts of suicide and social isolation among older adults in the United States},
	journal = {Journal of affective disorders},
	volume = {334},
	year = {2023},
	pages = {43-49},
	abstract = {BackgroundWe aimed to characterize the prevalence of social disconnection and thoughts of suicide among older adults in the United States, and examine the association between them in a large naturalistic study.MethodsWe analyzed data from 6 waves of a fifty-state non-probability survey among US adults conducted between February and December 2021. The internet-based survey collected the PHQ-9, as well as multiple measures of social connectedness. We applied\&nbsp;multiple logistic regression\&nbsp;to analyze the association between presence of thoughts of suicide and social disconnection. Exploratory analysis, using generalized random forests, examined heterogeneity of effects across sociodemographic groups.ResultsOf 16,164 survey respondents age 65 and older, mean age was 70.9 (SD 5.0); the cohort was 61.4\&nbsp;\% female and 29.6\&nbsp;\% male; 2.0\&nbsp;\% Asian, 6.7\&nbsp;\% Black, 2.2\&nbsp;\% Hispanic, and 86.8\&nbsp;\% White. A total of 1144 (7.1\&nbsp;\%) reported thoughts of suicide at least several days in the prior 2\&nbsp;week period. In models adjusted for sociodemographic features, households with 3 or more additional members (adjusted OR 1.73, 95\&nbsp;\% CI 1.28{\textendash}2.33) and lack of social supports, particularly emotional supports (adjusted OR 2.60, 95\&nbsp;\% CI 2.09{\textendash}3.23), were independently associated with greater likelihood of reporting such thoughts, as was greater reported loneliness (adjusted OR 1.75, 95\&nbsp;\% CI 1.64{\textendash}1.87). The effects of emotional support varied significantly across sociodemographic groups.ConclusionsThoughts of suicide are common among older adults in the US, and associated with lack of social support, but not with living alone.},
	url = {https://www.sciencedirect.com/science/article/pii/S0165032723004998},
	author = {Nili Solomonov and Jon Green and Alexi Quintana Math{\'e} and Jennifer Lin and Katherine Ognyanova and Santillana, Mauricio and James N Druckman and Matthew A Baum and Lazer, David and Faith M Gunning and Perlis, Roy H}
}
@article {710861,
	title = {Using General Messages to Persuade on a Politicized Scientific Issue},
	journal = {British Journal of Political Science},
	volume = {53},
	number = {2},
	year = {2023},
	pages = {698-706},
	abstract = {Politics and science have become increasingly intertwined. Salient scientific issues, such as climate change, evolution, and stem-cell research, become politicized, pitting partisans against one another. This creates a challenge of how to effectively communicate on such issues. Recent work emphasizes the need for tailored messages to specific groups. Here, we focus on whether generalized messages also can matter. We do so in the context of a highly polarized issue: extreme COVID-19 vaccine resistance. The results show that science-based, moral frame, and social norm messages move behavioral intentions, and do so by the same amount across the population (that is, homogeneous effects). Counter to common portrayals, the politicization of science does not preclude using broad messages that resonate with the entire population.},
	url = {https://www.cambridge.org/core/journals/british-journal-of-political-science/article/using-general-messages-to-persuade-on-a-politicized-scientific-issue/6AE0FF9C739061ED8F1BE379D7B2998A},
	author = {Jon Green and James N Druckman and Matthew A Baum and Lazer, David and Katherine Ognyanova and Matthew D Simonson and Jennifer Lin and Santillana, Mauricio and Perlis, Roy H}
}
@article {710856,
	title = {Estimating the impact of the COVID-19 pandemic on dengue in Brazil},
	journal = {Research Square},
	year = {2023},
	abstract = {Atypical dengue prevalence was observed in 2020 in many dengue-endemic countries, including Brazil. Evidence suggests that the pandemic disrupted not only dengue dynamics due to changes in mobility patterns, but also several aspects of dengue surveillance, such as care seeking behavior, care availability, and monitoring systems. However, we lack a clear understanding of the overall impact on dengue in different parts of the country as well as the role of individual causal drivers. In this study, we estimated the gap between expected and observed dengue cases in 2020 using an interrupted time series design with forecasts from a neural network and a structural Bayesian time series model. We also decomposed the gap into the impacts of climate conditions, pandemic-induced changes in reporting, human susceptibility, and human mobility. We find that there is considerable variation across the country in both overall pandemic impact on dengue and the relative importance of individual drivers. Increased understanding of the causal mechanisms driving the 2020 dengue season helps mitigate some of the data gaps caused by the COVID-19 pandemic and is critical to developing effective public health interventions to control dengue in the future.},
	url = {https://assets.researchsquare.com/files/rs-2548491/v1_covered.pdf?c=1678704454},
	author = {Kirstin Oliveira Roster and Tiago Martinelli and Colm Connaughton and Santillana, Mauricio and Francisco Rodrigues}
}
@article {710851,
	title = {Association of Post{\textendash}COVID-19 Condition Symptoms and Employment Status},
	journal = {JAMA network open},
	year = {2023},
	abstract = {Importance\&nbsp;\&nbsp;Little is known about the functional correlates of post{\textendash}COVID-19 condition (PCC), also known as long COVID, particularly the relevance of neurocognitive symptoms.Objective\&nbsp;\&nbsp;To characterize prevalence of unemployment among individuals who did, or did not, develop PCC after acute infection.Design, Setting, and Participants\&nbsp;\&nbsp;This survey study used data from 8 waves of a 50-state US nonprobability internet population-based survey of respondents aged 18 to 69 years conducted between February 2021 and July 2022.Main Outcomes and Measures\&nbsp;\&nbsp;The primary outcomes were self-reported current employment status and the presence of PCC, defined as report of continued symptoms at least 2 months beyond initial month of symptoms confirmed by a positive COVID-19 test.Results\&nbsp;\&nbsp;The cohort included 15 308 survey respondents with test-confirmed COVID-19 at least 2 months prior, of whom 2236 (14.6\%) reported PCC symptoms, including 1027 of 2236 (45.9\%) reporting either brain fog or impaired memory. The mean (SD) age was 38.8 (13.5) years; 9679 respondents (63.2\%) identified as women and 10 720 (70.0\%) were White. Overall, 1418 of 15 308 respondents (9.3\%) reported being unemployed, including 276 of 2236 (12.3\%) of those with PCC and 1142 of 13 071 (8.7\%) of those without PCC; 8229 respondents (53.8\%) worked full-time, including 1017 (45.5\%) of those with PCC and 7212 (55.2\%) without PCC. In survey-weighted regression models excluding retired respondents, the presence of PCC was associated with a lower likelihood of working full-time (odds ratio [OR], 0.71 [95\% CI, 0.63-0.80]; adjusted OR, 0.84 [95\% CI, 0.74-0.96]) and with a higher likelihood of being unemployed (OR, 1.45 [95\% CI, 1.22-1.73]; adjusted OR, 1.23 [95\% CI, 1.02-1.48]). The presence of any cognitive symptom was associated with lower likelihood of working full time (OR, 0.70 [95\% CI, 0.56-0.88]; adjusted OR, 0.75 [95\% CI, 0.59-0.84]).Conclusions and Relevance\&nbsp;\&nbsp;PCC was associated with a greater likelihood of unemployment and lesser likelihood of working full time in adjusted models. The presence of cognitive symptoms was associated with diminished likelihood of working full time. These results underscore the importance of developing strategies to treat and manage PCC symptoms.},
	url = {https://jamanetwork.com/journals/jamanetworkopen/article-abstract/2801458},
	author = {Perlis, Roy H and Trujillo, Kristin Lunz and Safarpour, Alauna and Santillana, Mauricio and Katherine Ognyanova and James Druckman and Lazer, David}
}
@article {710846,
	title = {Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study},
	journal = {JMIR Public Health and Surveillance},
	volume = {9},
	year = {2023},
	abstract = {Background:\&nbsp;Disease surveillance systems capable of producing accurate real-time and short-term forecasts can help public health officials design timely public health interventions to mitigate the effects of disease outbreaks in affected populations. In France, existing clinic-based disease surveillance systems produce gastroenteritis activity information that lags real time by 1 to 3 weeks. This temporal data gap prevents public health officials from having a timely epidemiological characterization of this disease at any point in time and thus leads to the design of interventions that do not take into consideration the most recent changes in dynamics.Objective:\&nbsp;The goal of this study was to evaluate the feasibility of using internet search query trends and electronic health records to predict acute gastroenteritis (AG) incidence rates in near real time, at the national and regional scales, and for long-term forecasts (up to 10 weeks).Methods:\&nbsp;We present 2 different approaches (linear and nonlinear) that produce real-time estimates, short-term forecasts, and long-term forecasts of AG activity at 2 different spatial scales in France (national and regional). Both approaches leverage disparate data sources that include disease-related internet search activity, electronic health record data, and historical disease activity.Results:\&nbsp;Our results suggest that all data sources contribute to improving gastroenteritis surveillance for long-term forecasts with the prominent predictive power of historical data owing to the strong seasonal dynamics of this disease.Conclusions:\&nbsp;The methods we developed could help reduce the impact of the AG peak by making it possible to anticipate increased activity by up to 10 weeks.},
	url = {https://publichealth.jmir.org/2023/1/e34982},
	author = {Canelle Poirier and Guillaume Bouzill{\'e} and Val{\'e}rie Bertaud and Marc Cuggia and Santillana, Mauricio and Audrey Lavenu}
}
@article {710841,
	title = {Correlates of symptomatic remission among individuals with post-COVID-19 condition},
	journal = {medRxiv},
	year = {2023},
	abstract = {Importance:\&nbsp;Post-COVID-19 condition (PCC), or long COVID, has become prevalent. The course of this syndrome, and likelihood of remission, has not been characterized.\&nbsp;Objective:\&nbsp;To quantify the rates of remission of PCC, and the sociodemographic features associated with remission.Design:\&nbsp;16 waves of a 50-state U.S. non-probability internet survey conducted between August 2020 and November 2022Setting:\&nbsp;Population-basedParticipants:\&nbsp;Survey respondents age 18 and olderMain Outcome and Measure:\&nbsp;PCC remission, defined as reporting full recovery from COVID-19 symptoms among individuals who on a prior survey wave reported experiencing continued COVID-19 symptoms beyond 2 months after the initial month of symptoms.\&nbsp;Results:\&nbsp;Among 423 survey respondents reporting continued symptoms more than 2 months after acute test-confirmed COVID-19 illness, who then completed at least 1 subsequent survey, mean age was 53.7 (SD 13.6) years; 293 (69\%) identified as women, and 130 (31\%) as men; 9 (2\%) identified as Asian, 29 (7\%) as Black, 13 (3\%) as Hispanic, 15 (4\%) as another category including Native American or Pacific Islander, and the remaining 357 (84\%) as White. Overall, 131/423 (31\%) of those who completed a subsequent survey reported no longer being symptomatic. In Cox regression models, male gender, younger age, lesser impact of PCC symptoms at initial visit, and infection when the Omicron strain predominated were all statistically significantly associated with greater likelihood of remission; presence of {\textquoteleft}brain fog{\textquoteright} or shortness of breath were associated with lesser likelihood of remission.Conclusions and Relevance:\&nbsp;A minority of individuals reported remission of PCC symptoms, highlighting the importance of efforts to identify treatments for this syndrome or means of preventing it.},
	url = {https://www.medrxiv.org/content/10.1101/2023.01.31.23285246v1.full.pdf},
	author = {Perlis, Roy H and Santillana, Mauricio and Katherine Ognyanova and Lazer, David}
}
@article {710821,
	title = {The evolving roles of US political partisanship and social vulnerability in the COVID-19 pandemic from February 2020{\textendash}February 2021},
	journal = {PLOS global public health},
	year = {2022},
	abstract = {The COVID-19 pandemic has had intense, heterogeneous impacts on different communities and geographies in the United States. We explore county-level associations between COVID-19 attributed deaths and social, demographic, vulnerability, and political variables to develop a better understanding of the evolving roles these variables have played in relation to mortality. We focus on the role of political variables, as captured by support for either the Republican or Democratic presidential candidates in the 2020 elections and the stringency of state-wide governor mandates, during three non-overlapping time periods between February 2020 and February 2021. We find that during the first three months of the pandemic, Democratic-leaning and internationally-connected urban counties were affected. During subsequent months (between May and September 2020), Republican counties with high percentages of Hispanic and Black populations were most hardly hit. In the third time period {\textendash}between October 2020 and February 2021{\textendash} we find that Republican-leaning counties with loose mask mandates experienced up to 3 times higher death rates than Democratic-leaning counties, even after controlling for multiple social vulnerability factors. Some of these deaths could perhaps have been avoided given that the effectiveness of non-pharmaceutical interventions in preventing uncontrolled disease transmission, such as social distancing and wearing masks indoors, had been well-established at this point in time.},
	url = {https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0000557},
	author = {Justin Kaashoek and Christian Testa and Jarvis T. Chen and Lucas M Stolerman and Krieger, Nancy and William P Hanage and Santillana, Mauricio}
}
@article {710816,
	title = {Evaluating the generalizability of the COVID States survey - a large-scale, non-probability survey},
	journal = {OSF Preprints},
	year = {2022},
	abstract = {
	COVID-19 fundamentally changed the world in a matter of months. To understand how it was impacting life in the United States, we fielded a non-probability survey in all 50 states concerning people{\textquoteright}s attitudes, beliefs, and behaviors, designed to be representative at the state level. Here, we evaluate the generalizability of this study by assessing the representativeness and convergent validity of our estimates. First, we evaluate the representativeness of the sample by comparing it to baseline estimates and auditing the size of the weights we use to reduce bias. We find our sample is diverse and most weights are below levels of concern with the exception of Hispanic respondents. Second, we assess the convergent validity of our survey by evaluating how our estimates of attitudes, behaviors, and opinions compare to estimates from other surveys and administrative data. Third, we perform a direct comparison of our results to the Kaiser Family Foundation{\textquoteright}s probability-based COVID-19 Vaccine Monitor. Overall, our estimates deviate from others by 1\%-7\% with the larger differences stemming from states with small populations and few other data sources and estimates from items with differing question wording or response choices. Here, we put forward a standard for evaluating the representativeness of surveys, non-probability or otherwise.

},
	url = {https://www.mighte.org/uploads/1/5/5/4/15543620/covid_states_validation_study.pdf},
	author = {Jason Radford and Jon Green and Alexi Quintana and Safarpour, Alauna and Matthew Simonson and Matthew Baum and Lazer, David and Katya Ognyanova and James N. Druckman and Roy Perlis and Santillana, Mauricio and John Della Volpe}
}
@article {710811,
	title = {Prevalence and Correlates of Long COVID Symptoms Among US Adults},
	journal = {JAMA Netw Open},
	year = {2022},
	abstract = {
	Importance\&nbsp;\&nbsp;Persistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection.



	Objectives\&nbsp;\&nbsp;To estimate the prevalence of and sociodemographic factors associated with long COVID and to identify whether the predominant variant at the time of infection and prior vaccination status are associated with differential risk.



	Design, Setting, and Participants\&nbsp;\&nbsp;This cross-sectional study comprised 8 waves of a nonprobability internet survey conducted between February 5, 2021, and July 6, 2022, among individuals aged 18 years or older, inclusive of all 50 states and the District of Columbia.



	Main Outcomes and Measures\&nbsp;\&nbsp;Long COVID, defined as reporting continued COVID-19 symptoms beyond 2 months after the initial month of symptoms, among individuals with self-reported positive results of a polymerase chain reaction test or antigen test.



	Results\&nbsp;\&nbsp;The 16 091 survey respondents reporting test-confirmed COVID-19 illness at least 2 months prior had a mean age of 40.5 (15.2) years; 10 075 (62.6\%) were women, and 6016 (37.4\%) were men; 817 (5.1\%) were Asian, 1826 (11.3\%) were Black, 1546 (9.6\%) were Hispanic, and 11 425 (71.0\%) were White. From this cohort, 2359 individuals (14.7\%) reported continued COVID-19 symptoms more than 2 months after acute illness. Reweighted to reflect national sociodemographic distributions, these individuals represented 13.9\% of those who had tested positive for COVID-19, or 1.7\% of US adults. In logistic regression models, older age per decade above 40 years (adjusted odds ratio [OR], 1.15; 95\% CI, 1.12-1.19) and female gender (adjusted OR, 1.91; 95\% CI, 1.73-2.13) were associated with greater risk of persistence of long COVID; individuals with a graduate education vs high school or less (adjusted OR, 0.67; 95\% CI, 0.56-0.79) and urban vs rural residence (adjusted OR, 0.74; 95\% CI, 0.64-0.86) were less likely to report persistence of long COVID. Compared with ancestral COVID-19, infection during periods when the Epsilon variant (OR, 0.81; 95\% CI, 0.69-0.95) or the Omicron variant (OR, 0.77; 95\% CI, 0.64-0.92) predominated in the US was associated with diminished likelihood of long COVID. Completion of the primary vaccine series prior to acute illness was associated with diminished risk for long COVID (OR, 0.72; 95\% CI, 0.60-0.86).



	Conclusions and Relevance\&nbsp;\&nbsp;This study suggests that long COVID is prevalent and associated with female gender and older age, while risk may be diminished by completion of primary vaccination series prior to infection.

},
	url = {https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2797782},
	author = {Roy H. Perlis and Santillana, Mauricio and Katherine Ognyanova and Safarpour, Alauna and Trujillo, Kristin Lunz and Simonson, Matthew D. and Jon Green and Alexi Quintana and James Druckman and Baum, Matthew A. and Lazer, David}
}
@article {709816,
	title = {Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of Coronavirus Disease 2019 in Africa},
	journal = {Clinical Infectious Diseases},
	volume = {76},
	number = {3},
	year = {2022},
	pages = {424-432},
	abstract = {Background\&nbsp;The COVID-19 pandemic has had a devastating impact on global health, the magnitude of which appears to differ intercontinentally: for example, reports suggest 271,900 per million people have been infected in Europe versus 8,800 per million people in Africa. While Africa is the second largest continent by population, its reported COVID-19 cases comprise \&lt;3\% of global cases. Although social, environmental, and environmental explanations have been proposed to clarify this discrepancy, systematic infection underascertainment may be equally responsible.Methods\&nbsp;We seek to quantify magnitudes of underascertainment in COVID-19{\textquoteright}s cumulative incidence in Africa. Using serosurveillance and postmortem surveillance, we constructed multiplicative factors estimating ratios of true infections to reported cases in Africa since March 2020.Results\&nbsp;Multiplicative factors derived from serology data (subset of 12 nations) suggested a range of COVID-19 reporting rates, from 1 in 2 infections reported in Cape Verde (July 2020) to 1 in 3,795 infections reported in Malawi (June 2020). A similar set of multiplicative factors for all nations derived from postmortem data points toward the same conclusion: reported COVID-19 cases are unrepresentative of true infections, suggesting a key reason for low case burden in many African nations is significant underdetection and underreporting.Conclusions\&nbsp;​While estimating COVID-19{\textquoteright}s exact burden is challenging, the multiplicative factors we present furnish incidence estimates reflecting likely-to-worst-case ranges of infection. Our results stress the need for expansive surveillance to allocate resources in areas experiencing discrepancies between reported cases, projected infections, and deaths.},
	url = {https://academic.oup.com/cid/article/76/3/424/6748257?login=false},
	author = {Kogan, Nicole E and Shae Gantt and David Swerdlow and C{\'e}cile Viboud and Semakula, Muhammed and Marc Lipsitch and Santillana, Mauricio}
}
@article {709811,
	title = {Methods for early characterisation of the severity and dynamics of SARS-CoV-2 variants: a population-based time series analysis in South Africa},
	journal = {Lancet Microbe },
	volume = {3},
	number = {10},
	year = {2022},
	pages = {753-761},
	abstract = {BackgroundAssessment of disease severity associated with a novel pathogen or variant provides crucial information needed by public health agencies and governments to develop appropriate responses. The SARS-CoV-2 omicron variant of concern (VOC) spread rapidly through populations worldwide before robust epidemiological and laboratory data were available to investigate its relative severity. Here we develop a set of methods that make use of non-linked, aggregate data to promptly estimate the severity of a novel variant, compare its characteristics with those of previous VOCs, and inform data-driven public health responses.MethodsUsing daily population-level surveillance data from the National Institute for Communicable Diseases in South Africa (March 2, 2020, to Jan 28, 2022), we determined lag intervals most consistent with time from case ascertainment to hospital admission and within-hospital death through optimisation of the distance correlation coefficient in a time series analysis. We then used these intervals to estimate and compare age-stratified case-hospitalisation and case-fatality ratios across the four epidemic waves that South Africa has faced, each dominated by a different variant.Findings​A total of 3 569 621 cases, 494 186 hospitalisations, and 99 954 deaths attributable to COVID-19 were included in the analyses. We found that lag intervals and disease severity were dependent on age and variant. At an aggregate level, fluctuations in cases were generally followed by a similar trend in hospitalisations within 7 days and deaths within 15 days. We noted a marked reduction in disease severity throughout the omicron period relative to previous waves (age-standardised case-fatality ratios were consistently reduced by \&gt;50\%), most substantial for age strata with individuals 50 years or older.},
	url = {https://www.sciencedirect.com/science/article/pii/S2666524722001823$\#$!},
	author = {Emily Reichert and Beau Schaeffer and Shae Gantt and Eva Rumpler and Nevashan Govender and Richard Welch and Andronica Moipone Shonhiwa and Chidozie Declan Iwu and Teresa Mashudu Lamola and Itumeleng Moema-Matiea and Darren Muganhiri and William Hanage and Santillana, Mauricio and Jassat, Waasila and Cheryl Cohen and David Swerdlow}
}
@article {709806,
	title = {Prediction of impending central-line-associated bloodstream infections in hospitalized cardiac patients: development and testing of a machine-learning model},
	journal = {Journal of Hospital Infection },
	volume = {127},
	year = {2022},
	pages = {44-50},
	abstract = {BackgroundWhile modelling of central-line-associated\&nbsp;blood stream infection\&nbsp;(CLABSI) risk factors is common, models that predict an impending CLABSI in real time are lacking.AimTo build a prediction model which identifies patients who will develop a CLABSI in the ensuing 24\&nbsp;h.MethodsWe collected variables potentially related to infection identification in all patients admitted to the cardiac intensive care unit or cardiac ward at Boston Children{\textquoteright}s Hospital in whom a\&nbsp;central venous catheter\&nbsp;(CVC) was in place between January 2010 and August 2020, excluding those with a diagnosis of bacterial\&nbsp;endocarditis. We created models predicting whether a patient would develop CLABSI in the ensuing 24\&nbsp;h. We assessed model performance based on area under the curve (AUC), sensitivity and false-positive rate (FPR) of models run on an independent testing set (40\%).FindingsA total of 104,035 patient-days and 139,662 line-days corresponding to 7468 unique patients were included in the analysis. There were 399 positive blood cultures (0.38\%), most commonly with\&nbsp;Staphylococcus aureus\&nbsp;(23\% of infections). Major predictors included a prior history of infection, elevated maximum heart rate, elevated maximum temperature, elevated C-reactive protein, exposure to\&nbsp;parenteral nutrition\&nbsp;and use of\&nbsp;alteplase\&nbsp;for CVC clearance. The model identified 25\% of positive cultures with an FPR of 0.11\% (AUC\&nbsp;= 0.82).ConclusionsA machine-learning model can be used to predict 25\% of patients with impending CLABSI with only 1.1/1000 of these predictions being incorrect. Once prospectively validated, this tool may allow for early treatment or prevention.},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S0195670122001906$\#$!},
	author = {K. Bonello and S. Emani and A. Sorensen and Shaw, L. and M. Godsay and M. Delgado and F. Sperotto and Santillana, M. and J.N. Kheir}
}
@article {709801,
	title = {Machine learning approaches to predicting no-shows in pediatric medical appointment},
	journal = {NPJ digital medicine },
	year = {2022},
	abstract = {Patients{\textquoteright} no-shows, scheduled but unattended medical appointments, have a direct negative impact on patients{\textquoteright} health, due to discontinuity of treatment and late presentation to care. They also lead to inefficient use of medical resources in hospitals and clinics. The ability to predict a likely no-show in advance could enable the design and implementation of interventions to reduce the risk of it happening, thus improving patients{\textquoteright} care and clinical resource allocation. In this study, we develop a new interpretable deep learning-based approach for predicting the risk of no-shows at the time when a medical appointment is first scheduled. The retrospective study was conducted in an academic pediatric teaching hospital with a 20\% no-show rate. Our approach tackles several challenges in the design of a predictive model by (1) adopting a data imputation method for patients with missing information in their records (77\% of the population), (2) exploiting local weather information to improve predictive accuracy, and (3) developing an interpretable approach that explains how a prediction is made for each individual patient. Our proposed neural network-based and logistic regression-based methods outperformed persistence baselines. In an unobserved set of patients, our method correctly identified 83\% of no-shows at the time of scheduling and led to a false alert rate less than 17\%. Our method is capable of producing meaningful predictions even when some information in a patient{\textquoteright}s records is missing. We find that patients{\textquoteright} past no-show record is the strongest predictor. Finally, we discuss several potential interventions to reduce no-shows, such as scheduling appointments of high-risk patients at off-peak times, which can serve as starting point for further studies on no-show interventions.},
	url = {https://www.nature.com/articles/s41746-022-00594-w},
	author = {Dianbo Liu and Won-Yong Shin and Eli Sprecher and Kathleen Conroy and Omar Santiago and Gal Wachtel and Santillana, Mauricio}
}
@article {709796,
	title = {Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data},
	journal = {PLoS computational biology },
	year = {2022},
	abstract = {When responding to infectious disease outbreaks, rapid and accurate estimation of the epidemic trajectory is critical. However, two common data collection problems affect the reliability of the epidemiological data in real time: missing information on the time of first symptoms, and retrospective revision of historical information, including right censoring. Here, we propose an approach to construct epidemic curves in near real time that addresses these two challenges by 1) imputation of dates of symptom onset for reported cases using a dynamically-estimated {\textquotedblleft}backward{\textquotedblright} reporting delay conditional distribution, and 2) adjustment for right censoring using the\&nbsp;NobBS\&nbsp;software package to nowcast cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (Rt) in real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between March and April 2020. We evaluate how these real-time estimates compare with more complete epidemiological data that became available later. We explore the impact of the different assumptions on the estimates, and compare our estimates with those obtained from commonly used surveillance approaches. Our framework can help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health systems in other locations.},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009964},
	author = {Pablo M De Salazar and Lu, Fred and James A Hay and Diana G{\'o}mez-Barroso and Pablo Fern{\'a}ndez-Navarro and Elena V Mart{\'\i}nez and Jenaro Astray-Mochales and Roc{\'\i}o Amillategui and Ana Garc{\'\i}a-Fulgueiras and Maria D Chirlaque and Alonso S{\'a}nchez-Migall{\'o}n and Amparo Larrauri and Mar{\'\i}a J Sierra and Marc Lipsitch and Fernando Sim{\'o}n and Santillana, Mauricio and Hern{\'a}n, Miguel A}
}
@article {709761,
	title = {Prevalence of Firearm Ownership Among Individuals With Major Depressive Symptoms},
	journal = {JAMA Netw Open},
	year = {2022},
	abstract = {Importance\&nbsp;\&nbsp;Both major depression and firearm ownership are associated with an increased risk for death by suicide in the United States, but the extent of overlap among these major risk factors is not well characterized.Objective\&nbsp;\&nbsp;To assess the prevalence of current and planned firearm ownership among individuals with depression.Design, Setting, and Participants\&nbsp;\&nbsp;Cross-sectional survey study using data pooled from 2 waves of a 50-state nonprobability internet survey conducted between May and July 7, 2021. Internet survey respondents were 18 years of age or older and were sampled from all 50 US states and the District of Columbia.Main Outcomes and Measures\&nbsp;\&nbsp;Self-reported firearm ownership; depressive symptoms as measured by the 9-item Patient Health Questionnaire.Results\&nbsp;\&nbsp;Of 24 770 survey respondents (64.6\% women and 35.4\% men; 5.0\% Asian, 10.8\% Black, 7.5\% Hispanic, and 74.0\% White; mean [SD] age 45.8 [17.5]), 6929 (28.0\%) reported moderate or greater depressive symptoms; this group had mean (SD) age of 38.18 (15.19) years, 4587 were female (66.2\%), and 406 were Asian (5.9\%), 725 were Black (10.5\%), 652 were Hispanic (6.8\%), and 4902 were White (70.7\%). Of those with depression, 31.3\% reported firearm ownership (n = 2167), of whom 35.9\% (n = 777) reported purchasing a firearm within the past year. In regression models, the presence of moderate or greater depressive symptoms was not significantly associated with firearm ownership (adjusted odds ratio [OR], 1.07; 95\% CI, 0.98-1.17) but was associated with greater likelihood of a first-time firearm purchase during the COVID-19 pandemic (adjusted OR, 1.77; 95\% CI, 1.56-2.02) and greater likelihood of considering a future firearm purchase (adjusted OR, 1.53; 95\% CI, 1.23-1.90).Conclusions and Relevance\&nbsp;\&nbsp;In this study, current and planned firearm ownership was common among individuals with major depressive symptoms, suggesting a public health opportunity to address this conjunction of suicide risk factors.},
	url = {https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2790171},
	author = {Roy H. Perlis and Simonson, Matthew D. and Jon Green and Jennifer Lin and Safarpour, Alauna and Trujillo, Kristin Lunz and Alexi Quintana and Hanyu Chwe and John Della Volpe and Katherine Ognyanova and Santillana, Mauricio and James Druckman and Lazer, David and Baum, Matthew A.}
}
@article {709756,
	title = {A machine-learning-guided adaptive algorithm to reduce the computational cost of integrating kinetics in global atmospheric chemistry models: application to GEOS-Chem versions 12.0.0 and 12.9.1},
	journal = {Geoscientific Model Development},
	volume = {15},
	number = {4},
	year = {2022},
	pages = {1687-2022},
	abstract = {Global modeling of atmospheric chemistry is a great computational challenge because of the cost of integrating the kinetic equations for chemical mechanisms with typically over 100 coupled species. Here we present an adaptive algorithm to ease this computational bottleneck with no significant loss in accuracy and apply it to the GEOS-Chem global 3-D model for tropospheric and stratospheric chemistry (228 species, 724 reactions). Our approach is inspired by unsupervised machine learning clustering techniques and traditional asymptotic analysis ideas. We locally define species in the mechanism as fast or slow on the basis of their total production and loss rates, and we solve the coupled kinetic system only for the fast species assembled in a submechanism of the full mechanism. To avoid computational overhead, we first partition the species from the full mechanism into 13 blocks, using a machine learning approach that analyzes the chemical linkages between species and their correlated presence as fast or slow in the global model domain. Building on these blocks, we then preselect 20 submechanisms, as defined by unique assemblages of the species blocks, and then pick locally and on the fly which submechanism to use in the model based on local chemical conditions. In each submechanism, we isolate slow species and slow reactions from the coupled system of fast species to be solved. Because many species in the full mechanism are important only in source regions, we find that we can reduce the effective size of the mechanism by 70 \% globally without sacrificing complexity where/when it is needed. The computational cost of the chemical integration decreases by 50 \% with relative biases smaller than 2 \% for important species over 8-year simulations. Changes to the full mechanism including the addition of new species can be accommodated by adding these species to the relevant blocks without having to reconstruct the suite of submechanisms.},
	url = {https://gmd.copernicus.org/articles/15/1677/2022/},
	author = {Shen, Lu and Daniel J. Jacob and Santillana, Mauricio and Kelvin Bates and Jiawei Zhuang and Chen, Wei}
}
@article {709751,
	title = {Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil},
	journal = {PLoS neglected tropical diseases},
	year = {2022},
	abstract = {The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people{\textquoteright}s lives and severely strain healthcare systems. In the absence of a reliable vaccine against dengue or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, scholarship on the topic had initially focused on prediction systems at the national- and state-levels, and much remains to be done at the finer spatial resolutions at which health policy interventions often occur. We develop a methodological framework to assess and compare dengue incidence estimates at the city level, and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that both random forest-based models and LASSO regression-based models effectively leverage these multiple data sources to produce accurate predictions, and that while the performance between them is comparable on average, the former method produces fewer extreme outliers, and can thus be considered more robust. For real-time predictions that assume long delays (6{\textendash}8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of dengue incidence, whereas for predictions that assume short delays (1{\textendash}3 weeks), in which the error rate is halved (as measured by relative RMSE), short-term and seasonal autocorrelation are the dominant predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different demographic, geographic and epidemic characteristics.},
	url = {https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0010071},
	author = {Gal Koplewitz and Lu, Fred and Clemente, Leonardo and Buckee, Caroline and Santillana, Mauricio}
}
@article {709746,
	title = {SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases},
	journal = {Science of The Total Environment},
	volume = {805},
	year = {2022},
	abstract = {Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban\&nbsp;wastewater treatment\&nbsp;facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4{\textendash}10\&nbsp;days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.},
	url = {https://www.sciencedirect.com/science/article/pii/S0048969721051962},
	author = {Fuqing Wu and Amy Xiao and Jianbo Zhang and Katya Moniz and Noriko Endo and Federica Armas and Bonneau, Richard and Megan A. Brown and Mary Bushman and Peter R. Chai and Claire Duvallet and Timothy B. Erickson and Katelyn Foppe and Newsha Ghaeli and Xiaoqiong Gu and William P. Hanage and Katherine H. Huang and Wei Lin Lee and Mariana Matus and Kyle A. McElroy and Jonathan Nagler and Steven F. Rhode and Santillana, Mauricio and Joshua A. Tucker and Stefan Wuertz and Shijie Zhao and Janelle Thompson and Eric J. Alm}
}
@article {709741,
	title = {Association of Major Depressive Symptoms With Endorsement of COVID-19 Vaccine Misinformation Among US Adults},
	journal = {JAMA Network Open},
	year = {2022},
	abstract = {Importance\&nbsp;\&nbsp;Misinformation about COVID-19 vaccination may contribute substantially to vaccine hesitancy and resistance.Objective\&nbsp;\&nbsp;To determine if depressive symptoms are associated with greater likelihood of believing vaccine-related misinformation.Design, Setting, and Participants\&nbsp;\&nbsp;This survey study analyzed responses from 2 waves of a 50-state nonprobability internet survey conducted between May and July 2021, in which depressive symptoms were measured using the Patient Health Questionnaire 9-item (PHQ-9). Survey respondents were aged 18 and older. Population-reweighted multiple logistic regression was used to examine the association between moderate or greater depressive symptoms and endorsement of at least 1 item of vaccine misinformation, adjusted for sociodemographic features. The association between depressive symptoms in May and June, and new support for misinformation in the following wave was also examined.Exposures\&nbsp;\&nbsp;Depressive symptoms.Main Outcomes and Measures\&nbsp;\&nbsp;The main outcome was endorsing any of 4 common vaccine-related statements of misinformation.Results\&nbsp;\&nbsp;Among 15 464 survey respondents (9834 [63.6\%] women and 5630 [36.4\%] men; 722 Asian respondents [4.7\%], 1494 Black respondents [9.7\%], 1015 Hispanic respondents [6.6\%], and 11 863 White respondents [76.7\%]; mean [SD] age, 47.9 [17.5] years), 4164 respondents (26.9\%) identified moderate or greater depressive symptoms on the PHQ-9, and 2964 respondents (19.2\%) endorsed at least 1 vaccine-related statement of misinformation. Presence of depression was associated with increased likelihood of endorsing misinformation (crude odds ratio [OR], 2.33; 95\% CI, 2.09-2.61; adjusted OR, 2.15; 95\% CI, 1.91-2.43). Respondents endorsing at least 1 misinformation item were significantly less likely to be vaccinated (crude OR, 0.40; 95\% CI, 0.36-0.45; adjusted OR, 0.45; 95\% CI, 0.40-0.51) and more likely to report vaccine resistance (crude OR, 2.54; 95\% CI, 2.21-2.91; adjusted OR, 2.68; 95\% CI, 2.89-3.13). Among 2809 respondents who answered a subsequent survey in July, presence of depression in the first survey was associated with greater likelihood of endorsing more misinformation compared with the prior survey (crude OR, 1.98; 95\% CI, 1.42-2.75; adjusted OR, 1.63; 95\% CI, 1.14-2.33).Conclusions and Relevance\&nbsp;\&nbsp;This survey study found that individuals with moderate or greater depressive symptoms were more likely to endorse vaccine-related misinformation, cross-sectionally and at a subsequent survey wave. While this study design cannot address causation, the association between depression and spread and impact of misinformation merits further investigation.},
	url = {https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2788284},
	author = {Roy H. Perlis and Katherine Ognyanova and Santillana, Mauricio and Jennifer Lin and James Druckman and Lazer, David and Jon Green and Matthew Simonson and Baum, Matthew A. and John Della Volpe}
}
@article {709736,
	title = {Characterizing features of outbreak duration for novel SARS-CoV-2 variants of concern},
	journal = {medRxiv},
	year = {2022},
	abstract = {
	Characterizing the dynamics of epidemic trajectories is critical to understanding the potential impacts of emerging outbreaks and to designing appropriate mitigation strategies. As the\&nbsp;COVID-19 pandemic evolves, however, the emergence of SARS-CoV-2 variants of concern has\&nbsp;complicated our ability to assess in real-time the potential effects of imminent outbreaks, such\&nbsp;as those presently caused by the Omicron variant. Here, we report that SARS-CoV-2 outbreaks\&nbsp;across regions exhibit strain-specific times from onset to peak, specifically for Delta and\&nbsp;Omicron variants. Our findings may facilitate real-time identification of peak medical demand and may help fine-tune ongoing and future outbreak mitigation deployment efforts.

},
	url = {https://www.medrxiv.org/content/10.1101/2022.01.14.22269288v1.full.pdf},
	author = {Alex D. Washburne and Nathaniel Hupert and Nicole Kogan and William Hanage and Santillana, Mauricio}
}
@article {709731,
	title = {Using General Messages to Persuade on a Politicized Scientific Issue},
	journal = {British Journal of Political Science},
	volume = {53},
	number = {2},
	year = {2022},
	pages = {698-706},
	abstract = {Politics and science have become increasingly intertwined. Salient scientific issues, such as climate change, evolution, and stem-cell research, become politicized, pitting partisans against one another. This creates a challenge of how to effectively communicate on such issues. Recent work emphasizes the need for tailored messages to specific groups. Here, we focus on whether generalized messages also can matter. We do so in the context of a highly polarized issue: extreme COVID-19 vaccine resistance. The results show that science-based, moral frame, and social norm messages move behavioral intentions, and do so by the same amount across the population (that is, homogeneous effects). Counter to common portrayals, the politicization of science does not preclude using broad messages that resonate with the entire population.},
	url = {https://www.cambridge.org/core/journals/british-journal-of-political-science/article/abs/using-general-messages-to-persuade-on-a-politicized-scientific-issue/6AE0FF9C739061ED8F1BE379D7B2998A},
	author = {Jon Green and James N Druckman and Matthew A Baum and Lazer, David and Katherine Ognyanova and Matthew Simonson and Jennifer Lin and Santillana, Mauricio and Perlis, Roy H}
}
@article {709631,
	title = {A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles},
	volume = {18},
	number = {179},
	year = {2021},
	abstract = {Transmission of dengue fever depends on a complex interplay of human, cli-mate and mosquito dynamics, which often change in time and space. It is wellknown that its disease dynamics are highly influenced by multiple factorsincluding population susceptibility to infection as well as by microclimates:small-area climatic conditions which create environments favourable for thebreeding and survival of mosquitoes. Here, we present a novel machine learn-ing dengue forecasting approach, which, dynamically in time and space,identifies local patterns in weather and population susceptibility to make epi-demic predictions at the city level in Brazil, months ahead of the occurrence ofdisease outbreaks. Weather-based predictions are improved when informationon population susceptibility is incorporated, indicating that immunity is animportant predictor neglected by most dengue forecast models. Given thegeneralizability of our methodology to any location or input data, it mayprove valuable for public health decision-making aimed at mitigating theeffects of seasonal dengue outbreaks in locations globally},
	url = {https://royalsocietypublishing.org/doi/epdf/10.1098/rsif.2020.1006},
	author = {Sarah F. McGough and Clemente, Leonardo and J. Nathan Kutz and Santillana, Mauricio}
}
@article {709626,
	title = {Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model},
	journal = {Pediatric Critical Care Medicine },
	volume = {22},
	number = {392},
	year = {2021},
	pages = {400},
	abstract = {
	Objectives:\&nbsp;



	To create a machine-learning model identifying potentially avoidable blood draws for serum potassium among pediatric patients following cardiac surgery.



	Design:\&nbsp;



	Retrospective cohort study.



	Setting:\&nbsp;



	Tertiary-care center.



	Patients:\&nbsp;



	All patients admitted to the cardiac ICU at Boston Children{\textquoteright}s Hospital between January 2010 and December 2018 with a length of stay greater than or equal to 4 days and greater than or equal to two recorded serum potassium measurements.



	Interventions:\&nbsp;



	None.



	Measurements and Main Results:\&nbsp;



	We collected variables related to potassium homeostasis, including serum chemistry, hourly potassium intake, diuretics, and urine output. Using established machine-learning techniques, including random forest classifiers, and hyperparameter tuning, we created models predicting whether a patient{\textquoteright}s potassium would be normal or abnormal based on the most recent potassium level, medications administered, urine output, and markers of renal function. We developed multiple models based on different age-categories and temporal proximity of the most recent potassium measurement. We assessed the predictive performance of the models using an independent test set. Of the 7,269 admissions (6,196 patients) included, serum potassium was measured on average of 1 (interquartile range, 0{\textendash}1) time per day. Approximately 96\% of patients received at least one dose of IV diuretic and 83\% received a form of potassium supplementation. Our models predicted a normal potassium value with a median positive predictive value of 0.900. A median percentage of 2.1\% measurements (mean 2.5\%; interquartile range, 1.3{\textendash}3.7\%) was incorrectly predicted as normal when they were abnormal. A median percentage of 0.0\% (interquartile range, 0.0{\textendash}0.4\%) critically low or high measurements was incorrectly predicted as normal. A median of 27.2\% (interquartile range, 7.8{\textendash}32.4\%) of samples was correctly predicted to be normal and could have been potentially avoided.



	Conclusions:\&nbsp;



	Machine-learning methods can be used to predict avoidable blood tests accurately for serum potassium in critically ill pediatric patients. A median of 27.2\% of samples could have been saved, with decreased costs and risk of infection or anemia.

},
	url = {https://journals.lww.com/pccmjournal/Fulltext/2021/04000/Avoidable_Serum_Potassium_Testing_in_the_Cardiac.6.aspx},
	author = {Bhaven B Patel and Francesca Sperotto and Mathieu Molina and Satoshi Kimura and Marlon I Delgado and Santillana, Mauricio and ohn N Kheir}
}
@article {709621,
	title = {A nowcasting framework for correcting for reporting delays in malaria surveillance},
	journal = {PLoS Computational Biology},
	volume = {2},
	number = {17},
	year = {2021},
	abstract = {Time lags in reporting to national surveillance systems represent a major barrier for the control of infectious diseases, preventing timely decision making and resource allocation. This issue is particularly acute for infectious diseases like malaria, which often impact rural and remote communities the hardest. In Guyana, a country located in South America, poor connectivity among remote malaria-endemic regions hampers surveillance efforts, making reporting delays a key challenge for elimination. Here, we analyze 13 years of malaria surveillance data, identifying key correlates of time lags between clinical cases occurring and being added to the central data system. We develop nowcasting methods that use historical patterns of reporting delays to estimate occurred-but-not-reported monthly malaria cases. To assess their performance, we implemented them retrospectively, using only information that would have been available at the time of estimation, and found that they substantially enhanced the estimates of malaria cases. Specifically, we found that the best performing models achieved up to two-fold improvements in accuracy (or error reduction) over known cases in selected regions. Our approach provides a simple, generalizable tool to improve malaria surveillance in endemic countries and is currently being implemented to help guide existing resource allocation and elimination efforts.},
	url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009570},
	author = {Tigist F. Menkir and Horace Cox and Canelle Poirier and Melanie Saul and Sharon Jones-Weekes {\textdagger} and Collette Clementson and Pablo M. de Salazar and Santillana, Mauricio and Caroline O. Buckee}
}
@article {709616,
	title = {Association Between Social Media Use and Self-reported Symptoms of Depression in US Adults},
	journal = {AMA network open},
	volume = {4},
	number = {11},
	year = {2021},
	abstract = {
	Importance\&nbsp;\&nbsp;Some studies suggest that social media use is associated with risk for depression, particularly among children and young adults.



	Objective\&nbsp;\&nbsp;To characterize the association between self-reported use of individual social media platforms and worsening of depressive symptoms among adults.



	Design, Setting, and Participants\&nbsp;\&nbsp;This survey study included data from 13 waves of a nonprobability internet survey conducted approximately monthly between May 2020 and May 2021 among individuals aged 18 years and older in the US. Data were analyzed in July and August 2021.



	Main Outcomes and Measures\&nbsp;\&nbsp;Logistic regression was applied without reweighting, with a 5 point or greater increase in 9-item Patient Health Questionnaire (PHQ-9) score as outcome and participant sociodemographic features, baseline PHQ-9, and use of each social media platform as independent variables.



	Results\&nbsp;\&nbsp;In total, 5395 of 8045 individuals (67.1\%) with a PHQ-9 score below 5 on initial survey completed a second PHQ-9. These respondents had a mean (SD) age of 55.8 (15.2) years; 3546 respondents (65.7\%) identified as female; 329 respondents (6.1\%) were Asian, 570 (10.6\%) Black, 256 (4.7\%) Hispanic, 4118 (76.3\%) White, and 122 (2.3\%) American Indian or Alaska Native, Pacific Islander or Native Hawaiian, or other. Among eligible respondents, 482 (8.9\%) reported 5 points or greater worsening of PHQ-9 score at second survey. In fully adjusted models for increase in symptoms, the largest adjusted odds ratio (aOR) associated with social media use was observed for Snapchat (aOR, 1.53; 95\% CI, 1.19-1.96), Facebook (aOR, 1.42; 95\% CI, 1.10-1.81), and TikTok (aOR, 1.39; 95\% CI, 1.03-1.87).



	Conclusions and Relevance\&nbsp;\&nbsp;Among survey respondents who did not report depressive symptoms initially, social media use was associated with greater likelihood of subsequent increase in depressive symptoms after adjustment for sociodemographic features and news sources. These data cannot elucidate the nature of this association, but suggest the need for further study to understand how social media use may factor into depression among adults.

},
	url = {https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2786464},
	author = {Roy H. Perlis and Jon Green and Matthew Simonson and Katherine Ognyanova and Santillana, Mauricio and ennifer Lin and Alexi Quintana and Hanyu Chwe and James Druckman and Lazer, David and Baum, Matthew A. and John Della Volpe}
}
@article {709611,
	title = {Gender-specificity of resilience in major depressive disorder   },
	journal = {Depression and anxiety },
	volume = {38},
	number = {10},
	year = {2021},
	pages = {1026-1033},
	abstract = {IntroductionThe major stressors associated with the COVID-19 pandemic provide an opportunity to understand the extent to which protective factors against depression may exhibit gender-specificity.MethodThis study examined responses from multiple waves of a 50 states non-probability internet survey conducted between May 2020 and January 2021. Participants completed the PHQ-9 as a measure of depression, as well as items characterizing social supports. We used logistic regression models with population reweighting to examine association between absence of even mild depressive symptoms and sociodemographic features and social supports, with interaction terms and stratification used to investigate sex-specificity.ResultsAmong 73,917 survey respondents, 31,199 (42.2\%) reported absence of mild or greater depression{\textemdash}11,011/23,682 males (46.5\%) and 20,188/50,235 (40.2\%) females. In a regression model, features associated with greater likelihood of depression-resistance included at least weekly attendance of religious services (odds ratio [OR]: 1.10, 95\% confidence interval [CI]: 1.04{\textendash}1.16) and greater trust in others (OR: 1.04 for a 2-unit increase, 95\% CI: 1.02{\textendash}1.06), along with level of social support measured as number of social ties available who could provide care (OR: 1.05, 95\% CI: 1.02{\textendash}1.07), talk to them (OR: 1.10, 95\% CI: 1.07{\textendash}1.12), and help with employment (OR: 1.06, 95\% CI: 1.04{\textendash}1.08). The first two features showed significant interaction with gender (p \&lt; .0001), with markedly greater protective effects among women.ConclusionAspects of social support are associated with diminished risk of major depressive symptoms, with greater effects of religious service attendance and trust in others observed among women than men.},
	url = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/da.23203},
	author = {Roy H. Perlis and Katherine Ognyanova and Alexi Quintana and Jon Green and Santillana, Mauricio and Jennifer Lin and James Druckman and Lazer, David and Matthew D Simonson and Matthew A Baum and Hanyu Chwe}
}
@article {709606,
	title = {Comparison of post-COVID depression and major depressive disorder},
	journal = { medRxiv },
	year = {2021},
	abstract = {
	Background:\&nbsp;During the COVID-19 pandemic rates of depressive symptoms are markedly elevated, particularly among survivors of infection. Understanding whether such symptoms are distinct among those with prior SARS-CoV-2 infection, or simply a nonspecific reflection of elevated stress, could help target interventions.\&nbsp;Method:\&nbsp;We analyzed data from multiple waves of a 50-state survey that included questions about COVID-19 infection as well as the Patient Health Questionnaire examining depressive and anxious symptoms. We utilized multiple logistic regression to examine whether sociodemographic features associated with depression liability differed for those with or without prior COVID-19, and then whether depressive symptoms differed among those with or without prior COVID- 19.



	Results:\&nbsp;Among 91,791 respondents, in regression models, age, gender, race, education, and income all exhibited an interaction with prior COVID-19 in risk for moderate or greater depressive symptoms (p\&lt;0.0001 in all cases), indicating differential risk in the two subgroups. Among those with such symptoms, levels of motoric symptoms and suicidality were significantly greater among those with prior COVID-19 illness. Depression risk increased with greater interval following acute infection.



	Discussion:\&nbsp;Our results suggest that major depressive symptoms observed among individuals with prior COVID-19 illness may not reflect typical depressive episodes, and merit more focused neurobiological investigation.

},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020988/pdf/nihpp-2021.03.26.21254425.pdf},
	author = {Roy H. Perlis and Santillana, Mauricio and Katherine Ognyanova and Jon Green and James Druckman and Lazer, David and Baum, Matthew A.}
}
@article {709586,
	title = {A machine learning-guided adaptive algorithm to reduce the computational cost of atmospheric chemistry in Earth System models: application to GEOS-Chem version 12.0.0 and v12.9.1 },
	journal = {Geosci. Model Dev. Discuss},
	volume = {12},
	year = {2021},
	abstract = {
	Atmospheric composition plays a crucial role in determining the evolution of the atmosphere, but the high computational cost has been the major barrier to include atmospheric chemistry into Earth system models. Here we present an adaptive and efficient algorithm that can remove this barrier. Our approach is inspired by unsupervised machine learning clustering techniques and traditional asymptotic analysis ideas. We first partition species into 13 blocks, using a novel



	15 machine learning approach that analyzes the species network structures and their production and loss rates. Building on these blocks, we pre-select 20 submechanisms, as defined by unique assemblages of the species blocks, and then pick locally on the fly which submechanism to use based on local chemical conditions. In each submechanism, we isolate slow species and unimportant reactions from the coupled system. Application to a global 3-D model shows that we can cut the computational costs of the chemical integration by 50\% with accuracy losses smaller than 1\% that do not propagate in time. Tests show that



	20 this algorithm is highly chemically coherent making it easily portable to new models without compromising its performance. Our algorithm will significantly ease the computational bottleneck and will facilitate the development of next generation of earth system models.

},
	url = {https://acmg.seas.harvard.edu/sites/projects.iq.harvard.edu/files/acmg/files/shen2021_gmd.pdf},
	author = {Shen, Lu and Daniel J. Jacob and Santillana, Mauricio and Kelvin Bates and Jiawei Zhuang and Chen, Wei}
}
@article {709581,
	title = {Persistence of symptoms up to 10 months following acute COVID-19 illness},
	journal = {MedRxiv},
	year = {2021},
	abstract = {Importance: COVID-19 symptoms are increasingly recognized to persist among a subset of individual following acute infection, but features associated with this persistence are not well-understood. Objective: We aimed to identify individual features that predicted persistence of symptoms over at least 2 months at the time of survey completion. Design: Non-probability internet survey. Participants were asked to identify features of acute illness as well as persistence of symptoms at time of study completion. We used logistic regression models to examine association between sociodemographic and clinical features and persistence of symptoms at or beyond 2 months.​},
	url = {https://www.medrxiv.org/content/medrxiv/early/2021/03/08/2021.03.07.21253072.full.pdf},
	author = {Roy H. Perlis and Jon Green and Santillana, Mauricio and Lazer, David and Katherine Ognyanova and Matthew Simonson and Baum, Matthew A. and Alexi Quintana and Hanyu Chwe and James Druckman and John Della Volpe and Jennifer Lin}
}
@article {709576,
	title = {High-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S.  },
	journal = {ACM Transactions on Management Information Systems (TMIS)},
	volume = {12},
	number = {4},
	year = {2021},
	pages = {1-22},
	abstract = {We present an interpretable high-resolution spatio-temporal model to estimate COVID-19 deaths together with confirmed cases 1 week ahead of the current time, at the county level and weekly aggregated, in the United States. A notable feature of our spatio-temporal model is that it considers the (1) temporal auto- and pairwise correlation of the two local time series (confirmed cases and deaths from the COVID-19), (2) correlation between locations (propagation between counties), and (3) covariates such as local within-community mobility and social demographic factors. The within-community mobility and demographic factors, such as total population and the proportion of the elderly, are included as important predictors since they are hypothesized to be important in determining the dynamics of COVID-19. To reduce the model{\textquoteright}s high dimensionality, we impose sparsity structures as constraints and emphasize the impact of the top 10 metropolitan areas in the nation, which we refer to (and treat within our models) as hubs in spreading the disease. Our retrospective out-of-sample county-level predictions were able to forecast the subsequently observed COVID-19 activity accurately. The proposed multivariate predictive models were designed to be highly interpretable, with clear identification and quantification of the most important factors that determine the dynamics of COVID-19. Ongoing work involves incorporating more covariates, such as education and income, to improve prediction accuracy and model interpretability.},
	url = {https://dl.acm.org/doi/pdf/10.1145/3468876},
	author = {Shixiang Zhu and Alexander Bukharin and Liyan Xie and Santillana, Mauricio and Yang, Shihao and Yao Xie}
}
@article {678840,
	title = { The role of race, religion, and partisanship in misinformation about COVID-19},
	journal = {Group Processes \& Intergroup Relations},
	volume = {24},
	number = {4},
	year = {2021},
	pages = {638{\textendash}657},
	author = {James Druckman and Katherine Ognyanova and Matthew Baum and Lazer, David and Roy Perlis and John Della Volpe and Santillana, Mauricio and H Chwe and Alexi Quintana and Matthew Simonson}
}
@article {678839,
	title = {Factors associated with self-reported symptoms of depression among adults with and without a previous COVID-19 diagnosis},
	journal = {JAMA Network Open},
	volume = {4},
	number = {6},
	year = {2021},
	author = {Roy Perlis and Santillana, Mauricio and Katherine Ognyanova and Jon Green and James Druckman and Lazer, David and Matthew Baum}
}
@article {674062,
	title = {Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil},
	journal = {PLoS Neglected Tropical Diseases},
	year = {2021},
	abstract = {Dengue virus is spread through mosquitoes in many tropical and subtropical parts ofthe world, including Brazil. Each year, dengue virus causes seasonal outbreaks that varyin magnitude and timing across the country. This variation makes tailoring preparationefforts for fine spatio-temporal resolutions challenging. In this study, we described fourproperties of historical dengue time series at the mesoregion level, the Braziliansubdivision below state, and examined how they varied across the country. We foundthat the duration and timing of seasonal outbreaks are largely driven by climate factors,while relational properties, i.e., the similarity in outbreak timing and magnitudebetween two mesoregions, are explained by a mix of mobility patterns and climatesimilarities. Surprisingly, we found that remote sensing derived products and movementinferred through Twitter were adequate proxies for climate and mobility patternsrespectively. Knowledge of how dengue outbreaks differ across the country and thefactors that may influence specific outbreak properties may be important for improvingefforts to build forecasting and prediction models.},
	author = {Lauren A. Castro and Nicholas Generous and Wei Luo and Ana Pastore y Piontti and Kaitlyn Martinez and Marcelo F.C. Gomes and Dave Osthus and Geoffrey Fairchild and Amanda Ziemann and Alessandro Vespignani and Santillana, Mauricio and Carrie A. Manore and Sara Y. Del Valle}
}
@article {673429,
	title = {High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in Long-Term Care Facilities},
	journal = {Nature Communications Medicine},
	volume = {1},
	number = {16},
	year = {2021},
	abstract = {Residents of Long-Term Care Facilities (LTCFs) represent a major share of COVID-19 deaths worldwide. Information on vaccine effectiveness in these settings is essential to improve mitigation strategies, but evidence remains limited. To evaluate the early effect of the administration of BNT162b2 mRNA vaccines in LTCFs, we monitored subsequent SARS-CoV-2 documented infections and deaths in Catalonia, a region of Spain, and compared them to counterfactual model predictions from February 6th to March 28th, 2021, the subsequent time period after which 70\% of residents were fully vaccinated. We calculated the reduction in SARS-CoV-2 documented infections and deaths as well as the detected county-level transmission. We estimated that once more than 70\% of the LTCFs population were fully vaccinated, 74\% (58\%-81\%, 90\% CI) of COVID-19 deaths and 75\% (36\%-86\%) of all documented infections were prevented. Further, detectable transmission was reduced up to 90\% (76-93\% 90\%CI). Our findings provide evidence that high-coverage vaccination is the most effective intervention to prevent SARS-CoV-2 transmission and death. Widespread vaccination could be a feasible avenue to control the COVID-19 pandemic.},
	author = {Pablo de Salazar and Nicholas Link and Karuna Lamarca and Santillana, Mauricio}
}
@article {672960,
	title = {Association of Acute Symptoms of COVID-19 and Symptoms of Depression in Adults},
	journal = {JAMA Network Open},
	volume = {4},
	number = {3},
	year = {2021},
	pages = {e213223},
	abstract = {After acute infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a subset of individuals experience persistent symptoms involving mood, sleep, anxiety, and fatigue, which may contribute to markedly elevated rates of major depressive disorder observed in recent epidemiologic studies. In this study, we investigated whether acute coronavirus disease 2019 (COVID-19) symptoms are associated with the probability of subsequent depressive symptoms.},
	author = {Roy H. Perlis and Katherine Ognyanova and Santillana, Mauricio and Baum, Matthew A. and Lazer, David and James Druckman and John Della Volpe}
}
@article {669354,
	title = {Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data},
	journal = {medRxiv},
	year = {2021},
	abstract = {Designing public health responses to outbreaks requires close monitoring of population-level health indicators in real-time. Thus an accurate estimation of the epidemic curve is critical. We propose an approach to reconstruct epidemic curves in near real time. We apply this approach to characterize the early SARS-CoV-2 outbreak in two Spanish regions between the months of March and April 2020. We address two data collection problems that affected the reliability of the available real-time epidemiological data, namely, the frequent missing information documenting when a patient first experienced symptoms, and the frequent retrospective revision of historical information (including right censoring). This is done by using a novel back-calculating procedure based on imputing patients dates of symptom onset from reported cases, according to a dynamically-estimated backward reporting delay conditional distribution, and adjusting for right censoring using an existing package, NobBS, to estimate in real time (nowcast) cases by date of symptom onset. This process allows us to obtain an approximation of the time-varying reproduction number (Rt) in real-time. At each step, we evaluate how different assumptions affect the recovered epidemiological events and compare the proposed approach to the alternative procedure of merely using curves of case counts, by report day, to characterize the time-evolution of the outbreak. Finally, we assess how these real-time estimates compare with subsequently documented epidemiological information that is considered more reliable and complete that became available weeks to months later in time. Our approach may help improve accuracy, quantify uncertainty, and evaluate frequently unstated assumptions when recovering the epidemic curves from limited data obtained from public health surveillance systems in other locations.},
	author = {Pablo Martinez de Salazar and Lu, Fred and James A Hay and Diana Gomez-Barroso and Fernandez-Navarro, Pablo and Elena Vanessa Martinez and Jenaro Astray-Mochales and Rocio Amillategui and Ana Garcia-Fulgueiras and Maria Dolores Chirlaque and Alonso Sanchez-Migallon and Amparo Larrauri and Maria Jose Sierra and Marc Lipsitch and Fernando Simon and Santillana, Mauricio and Miguel Hernan}
}
@article {669091,
	title = {Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile},
	journal = {Science},
	year = {2021},
	pages = {eabg5298},
	abstract = {The current COVID-19 pandemic has impacted cities particularly hard. Here, we provide an in-depth characterization of disease incidence and mortality, and their dependence on demographic and socioeconomic strata in Santiago, a highly segregated city and the capital of Chile. Our analyses show a strong association between socioeconomic status and both COVID-19 outcomes and public health capacity. People living in municipalities with low socioeconomic status did not reduce their mobility during lockdowns as much as those in more affluent municipalities. Testing volumes may have been insufficient early in the pandemic in those places, and both test positivity rates and testing delays were much higher. We find a strong association between socioeconomic status and mortality, measured either by COVID-19 attributed deaths or excess deaths. Finally, we show that infection fatality rates in young people are higher in low-income municipalities. Together, these results highlight the critical consequences of socioeconomic inequalities on health outcomes.},
	author = {Gonzalo Mena and Pamela P. Martinez and Mahmud, Ayesha S. and Pablo A. Marquet and Caroline O. Buckee and Santillana, Mauricio}
}
@article {660588,
	title = {Incorporating human mobility data improves forecasts of Dengue fever in Thailand},
	journal = {Scientific Reports},
	volume = {11},
	number = {923},
	year = {2021},
	abstract = {Over 390 million people worldwide are infected with dengue fever each year. In the absence of an effective vaccine for general use, national control programs must rely on hospital readiness and targeted vector control to prepare for epidemics, so accurate forecasting remains an important goal. Many dengue forecasting approaches have used environmental data linked to mosquito ecology to predict when epidemics will occur, but these have had mixed results. Conversely, human mobility, an important driver in the spatial spread of infection, is often ignored. Here we compare time-series forecasts of dengue fever in Thailand, integrating epidemiological data with mobility models generated from mobile phone data. We show that long-distance connectivity is correlated with dengue incidence at forecasting horizons of up to three months, and that incorporating mobility data improves traditional time-series forecasting approaches. Notably, no single model or class of model always outperformed others. We propose an adaptive, mosaic forecasting approach for early warning systems.},
	author = {Mathew V. Kiang and Santillana, Mauricio and Jarvis T. Chen and Jukka-Pekka Onnela and Krieger, Nancy and Kenth Eng{\o}-Monsen and Nattwut Ekapirat and Darin Areechokchai and Richard Maude and Caroline O. Buckee}
}
@article {655410,
	title = {An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time},
	journal = {Science Advances},
	volume = {7},
	number = {10},
	year = {2021},
	abstract = {Given still-high levels of coronavirus disease 2019 (COVID-19) susceptibility and inconsistent transmission-containing strategies, outbreaks have continued to emerge across the United States. Until effective vaccines are widely deployed, curbing COVID-19 will require carefully timed nonpharmaceutical interventions (NPIs). A COVID-19 early warning system is vital for this. Here, we evaluate digital data streams as early indicators of state-level COVID-19 activity from 1 March to 30 September 2020. We observe that increases in digital data stream activity anticipate increases in confirmed cases and deaths by 2 to 3 weeks. Confirmed cases and deaths also decrease 2 to 4 weeks after NPI implementation, as measured by anonymized, phone-derived human mobility data. We propose a means of harmonizing these data streams to identify future COVID-19 outbreaks. Our results suggest that combining disparate health and behavioral data may help identify disease activity changes weeks before observation using traditional epidemiological monitoring.},
	author = {Nicole E. Kogan and Clemente, Leonardo and Parker Liautaud and Justin Kaashoek and Nicholas B. Link and Andre T. Nguyen and Fred S. Lu and Huybers, Peter and Bernd Resch and Clemens Havas and Andreas Petutschnig and Jessica Davis and Matteo Chinazzi and Backtosch Mustafa and William P. Hanage and Alessandro Vespignani and Santillana, Mauricio}
}
@article {651623,
	title = {Influenza forecasting for the French regions by using EHR, web and climatic data sources with an ensemble approach},
	journal = {PLoS One},
	year = {2021},
	abstract = {Effective and timely disease surveillance systems have the potential to help public health officials design interventions to mitigate the effects of disease outbreaks. Currently, healthcare-based disease monitoring systems in France offer influenza activity information that lags real-time by 1 to 3 weeks. This temporal data gap introduces uncertainty that prevents public health officials from having a timely perspective on the population-level disease activity. Here, we present a machine-learning modeling approach that produces real-time estimates and short-term forecasts of influenza activity for the 12 continental regions of France by leveraging multiple disparate data sources that include, Google search activity, real-time and local weather information, flu-related Twitter micro-blogs, electronic health records data, and historical disease activity synchronicities across regions. Our results show that all data sources contribute to improving influenza surveillance and that machine-learning ensembles that combine all data sources lead to accurate and timely predictions.},
	author = {Canelle Poirier and Yulin Hswen and Guillaume Bouzille and Marc Cuggia and Audrey Lavenu and Brownstein, John S and Brewer, Thomas and Santillana, Mauricio}
}
@article {651504,
	title = {Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches},
	journal = {PLoS Computational Biology },
	year = {2021},
	abstract = {Effectively designing and evaluating public health responses to the ongoing COVID-19 pan-demic \&nbsp;requires \&nbsp;accurate \&nbsp;estimation \&nbsp;of \&nbsp;the \&nbsp;prevalence \&nbsp;of \&nbsp;COVID-19 \&nbsp;across \&nbsp;the \&nbsp;United \&nbsp;States(US). Equipment shortages and varying testing capabilities have however hindered the useful-ness of the official reported positive COVID-19 case counts. We introduce four complementaryapproaches to estimate the cumulative incidence of symptomatic COVID-19 in each state inthe \&nbsp;US \&nbsp;as \&nbsp;well \&nbsp;as \&nbsp;Puerto \&nbsp;Rico \&nbsp;and \&nbsp;the \&nbsp;District \&nbsp;of \&nbsp;Columbia, \&nbsp;using \&nbsp;a \&nbsp;combination \&nbsp;of \&nbsp;excessinfluenza-like illness reports, COVID-19 test statistics, COVID-19 mortality reports, and a spatially structured epidemic model. Instead of relying on the estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our four approaches emerges the consistent conclusion that on April 4, 2020, the estimated case count was 5 to 50 times higher than the official positive testcounts across the different states. Nationally, our estimates of COVID-19 symptomatic cases asof April 4 have a likely range of 2.3 to 4.8 million, with possibly as many as 7.6 million cases,up to 25 times greater than the cumulative confirmed cases of about 311,000. Extending ourmethods to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 4.9 to 10.1 million, as opposed to 1.5 million positive test counts. The proposed combination ofapproaches may prove useful in assessing the burden of COVID-19 during resurgences in the US and other countries with comparable surveillance systems.},
	author = {Fred S. Lu and Andre T. Nguyen and Nick Link and Mathieu Molina and Jessica T Davis and Matteo Chinazzi and Xinyue Xiong and Alessandro Vespignani and Marc Lipsitch and Santillana, Mauricio}
}
@article {648299,
	title = {Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions},
	journal = {Science Advances},
	volume = {7},
	number = {25},
	year = {2021},
	abstract = {Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional healthcare-based surveil- lance systems are limited by inherent reporting delays. Time-series machine learning methods have the potential to fill this temporal {\textquotedblleft}data gap,{\textquotedblright} but work to date in this area has focused on relatively simple methods and coarse geographic granularities (state-level and above). We evaluate the performance of a recurrent neural network (gated recurrent unit, or GRU) in comparison to baseline machine learning methods for estimating influenza activity in the US on the state- and city-level, and experiment with the inclusion of real-time search data from Google trends. We find that the GRU improves upon baseline models for long time horizons of prediction but is not improved by real-time Internet search data. We conduct a thorough analysis of feature importance in all considered models for interpretability purposes.},
	author = {E L Aiken and Nguyen, AT and Cecile Viboud and Santillana, Mauricio}
}
@article {637084,
	title = {A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles},
	journal = {Journal of the Royal Society Interface},
	volume = {18},
	number = {179},
	year = {2021},
	abstract = {Transmission of dengue fever depends on a complex interplay of human, climate, and mosquito dynamics, which often change in time and space. It is well known that disease dynamics are highly influenced by a population{\textquoteright}s susceptibility to infection and microclimates, small-area climatic conditions which create environments favorable for the breeding and survival of the mosquito vector. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and adaptively in space, identifies local patterns in weather and population susceptibility to make epidemic predictions at the city-level in Brazil, months ahead of the occurrence of disease outbreaks. Weather-based predictions are improved when information on population susceptibility is incorporated, indicating that immunity is an important predictor neglected by most dengue forecast models. Given the generalizability of our methodology, it may prove valuable for public-health decision making aimed at mitigating the effects of seasonal dengue outbreaks in locations globally.},
	author = {Sarah McGough and Nathan J Kutz and Leonardo C Clemente and Santillana, Mauricio}
}
@article {673434,
	title = {Predicting Dengue Incidence Leveraging Internet-Based Data Sources. A Case Study in 20 cities in Brazil.},
	journal = {medRxiv},
	volume = {2020.10.21.20210948},
	year = {2020},
	abstract = {The dengue virus affects millions of people every year worldwide, causing large epidemic outbreaks that disrupt people{\textquoteright}s lives and severely strain healthcare systems. In the absence of a reliable vaccine against it or an effective treatment to manage the illness in humans, most efforts to combat dengue infections have focused on preventing its vectors, mainly the Aedes aegypti mosquito, from flourishing across the world. These mosquito-control strategies need reliable disease activity surveillance systems to be deployed. Despite significant efforts to estimate dengue incidence using a variety of data sources and methods, little work has been done to understand the relative contribution of the different data sources to improved prediction. Additionally, most work has focused on prediction systems at the national level, rather than at finer spatial resolutions. We develop a methodological framework to assess and compare dengue incidence estimates at the city level and evaluate the performance of a collection of models on 20 different cities in Brazil. The data sources we use towards this end are weekly incidence counts from prior years (seasonal autoregressive terms), weekly-aggregated weather variables, and real-time internet search data. We find that a random forest-based model effectively leverages these multiple data sources and provides robust predictions, while retaining interpretability. For real-time predictions that assume long delays (6-8 weeks) in the availability of epidemiological data, we find that real-time internet search data are the strongest predictors of Dengue incidence, whereas for predictions that assume very short delays (1-2 weeks), short-term and seasonal autocorrelation are dominant as predictors. Despite the difficulties inherent to city-level prediction, our framework achieves meaningful and actionable estimates across cities with different characteristics.},
	author = {Gal Koplewitz and Lu, Fred and Clemente, Leonardo and Buckee, Caroline and Santillana, Mauricio}
}
@article {665149,
	title = {COVID-19: US Federal accountability for entry, spread, and inequities {\textendash} lessons for the future},
	journal = {European Journal of Epidemiology},
	year = {2020},
	abstract = {The United States (US) has been among those nations most severely affected by the first{\textemdash}andsubsequent{\textemdash}phases of the pandemic of COVID-19 disease caused by SARS-CoV-2. With only4\% of the worldwide population, the US has seen about 22\% of COVID-19 deaths. Despiteformidable advantages in resources and expertise, presently the per capita mortality rate is over585/million, respectively 2.4 and 5 times higher compared to Canada and Germany. As we enterFall 2020, the US is enduring ongoing outbreaks across large regions of the country. Moreover,within the US, an early and persistent feature of the pandemic has been the disproportionateimpact on populations already made vulnerable by racism and dangerous jobs, inadequate wages,and unaffordable housing, and this is true for both the headline public health threat and theadditional disastrous economic impacts. In this article we assess the impact of missteps by theFederal Government in three specific areas: the introduction of the virus to the US and theestablishment of community transmission; the lack of national COVID-19 workplace standardsand lack of personal protective equipment (PPE) for workplaces as represented by complaints tothe Occupational Safety and Health Administration (OSHA) which we find are correlated withdeaths 17 days later (=0.845); and the total excess deaths in 2020 to date, which already totalmore than 230,000 and exhibit severe inequities in race/ethnicity including among younger agegroups.},
	url = {https://doi.org/10.1007/s10654-020-00689-2},
	author = {Hanage, WP and C Testa and J.T. Chen and L David and E Pechter and P Seminario and Santillana, M and Krieger, N}
}
@article {664701,
	title = {Avoidable serum potassium testing in the cardiac intensive care unit: development and testing of a machine learning model},
	journal = {Pediatric Critical Care Medicine},
	volume = {22},
	number = {4},
	year = {2020},
	abstract = {
	Objective:\&nbsp;To create a machine learning model identifying potentially avoidable blood draws forserum potassium among pediatric patients following cardiac surgery.



	Design:Retrospective cohort study.Setting:\&nbsp;Tertiary-care center.Patients:\&nbsp;All patients admitted to the CICU at Boston Children{\textquoteright}s Hospital between January 2010and December 2018 with a length of stay >=4 days and >=2 recorded serum potassiummeasurements.Interventions\&nbsp;None.Measurements and Main ResultsWe collected variables related to potassium homeostasis, including serum chemistry,hourly potassium intake, diuretics, and urine output. Using established machinelearning techniques, including Random Forest classifiers and hyperparameters, wecreated models predicting whether a patient{\textquoteright}s potassium would be normal or abnormalbased on the most recent potassium level, medications administered, urine output and markers of renal function. We developed multiple models based on different age-categories and temporal proximity of the most recent potassium measurement. We assessed the predictive performance of the models using an independent test set. Ofthe 7,269 admissions (6,196 patients) included, 95,674 serum potassium was measured on average of 1 (IQR 0-1) time per day. 96\% of patients received at leastone dose of IV diuretic and 83\% received a form of potassium supplementation. Our models predicted a normal potassium value with a median positive predictive value of 0.900. A median percentage of 2.1\% measurements (mean 2.5\%, IQR 1.3\%-3.7\%) were incorrectly predicted as normal when they were abnormal.A median percentage of 0.0\% (IQR 0.0\%-0.4\%) were critically low or high measurements were incorrectly predicted as normal. A median of 27.2\% (IQR 7.8\%-32.4\%) of samples were correctly predicted to be normal and could have been potentially avoided.ConclusionsMachine-learning methods can be used to accurately predict avoidable blood tests forserum potassium in critically ill pediatric patients. A median of 27.2\% of samples couldhave been saved, with decreased costs and risk of infection or anemia.

},
	author = {Bhaven Patel and Francesca Sperotto and Mathieu Molina and Satoshi Kimura and Marlon Delgado and Santillana, Mauricio and John Nagi Kheir}
}
@article {654708,
	title = {Communicating Benefits from Vaccines Beyond Preventing Infectious Diseases},
	journal = {Infectious Diseases and Therapy},
	volume = {9},
	year = {2020},
	pages = {467{\textendash}480},
	abstract = {Despite immunisation being one of the greatest medical success stories of the 20th century and its benefits being widely recognized there is a growing lack of confidence in some vaccines. Improving communication about the direct benefits of vaccination as well as its benefits beyond preventing infectious diseases may help regain this lost trust. A conference was organised at the Fondation Merieux in France to discuss what benefits could be communicated and how their communication could use innovative digital initiatives. During this meeting a wide range of poorly known indirect benefits of vaccination, including benefits for chronic non-communicable diseases (NCD). For example, persons with underlying chronic NCDs, such as diabetes and cardiovascular diseases, are particularly vulnerable to complications, hospitalisations, and even death from influenza, although the link between NCDs and influenza is frequently underestimated. Influenza vaccination can reduce hospitalizations and deaths in older persons with diabetes by 45\% and 38\% respectively. The frequency of antimicrobial resistance (AMR) is increasing worldwide. Vaccination can reduce AMR by reducing the incidence of infectious disease (though direct and indirect or herd protection), by reducing the number of circulating AMR strains, and by reducing the need for antimicrobial use. In addition, as the global population ages, disease morbidity and treatment costs in the elderly population are likely to rise substantially. The promotion of healthy ageing and adopting a life-course approach to health can reduce the burden of vaccine-preventable diseases such as seasonal influenza, pneumococcal diseases, meningitis, pertussis, shingles, measles, diphtheria and tetanus, which place a significant burden on individuals and the ageing society, and improve their quality of life. Novel disease surveillance systems based on information from Internet search-engines, mobile phone apps, social media, new reports, cloud-based electronic-health records, and crowd-sourced systems, contribute to an improved burden of disease awareness. Examples of the role of new techniques and tools to process data generated by multiple sources, such as artificial intelligence, advanced data analytics and biostatistics to support vaccination programmes, such as influenza and dengue were discussed. The conference participants agreed that continual efforts are needed from all stakeholders to ensure effective, transparent communication of the full benefits and risks of vaccines and vaccination and this will require continued dialogue and collaboration.},
	author = {Emma-Pascale~Chevalier-Cottin and Hayley~Ashbaugh and Nicholas~Brooke and Gaetan~Gavazzi and Mauricio~Santillana and Nansa~Burlet and Myint~Tin~Tin~Htar}
}
@article {651652,
	title = {Aggregated mobility data could help fight COVID-19},
	journal = {Science},
	volume = {368},
	number = {6487},
	year = {2020},
	pages = {145-146},
	abstract = {As the coronavirus disease 2019 (COVID-19) epidemic worsens, understanding the effectiveness of public messaging and large-scale social distancing interventions is critical. The research and public health response communities can and should use population mobility data collected by private companies, with appropriate legal, organizational, and computational safeguards in place. When aggregated, these data can help refine interventions by providing near real-time information about changes in patterns of human movement.},
	author = {Caroline O. Buckee and Balsari, Satchit and Chan, Jennifer and Crosas, Merc{\`e} and Francesca Dominici and Urs Gasser and Yonatan H. Grad and Grenfell, Bryan and Halloran, M. Elizabeth and Moritz U. G. Kraemer and Marc Lipsitch and C. Jessica E. Metcalf and Lauren Ancel Meyers and T. Alex Perkins and Santillana, Mauricio and Samuel V. Scarpino and Cecile Viboud and Amy Wesolowski and Schroeder, Andrew}
}
@article {651629,
	title = {Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multi-Center Study During the COVID-19 Outbreak},
	journal = {Cancer Discovery},
	volume = {DOI: 10.1158/2159-8290.CD-20-0422},
	year = {2020},
	abstract = {Background: The novel COVID-19 outbreak, caused by the SARS-CoV-2 virus and originally detected in December 2019 in Wuhan, China, has affected more than 140 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 are needed.Methods: Clinical information from 105 hospitalized patients with cancer and 233 hospitalized patients without cancer, all infected by the SARS-CoV-2 virus, were collected from 14 hospitals in Hubei province, China, from January 1, 2020, to February 24, 2020. Standard statistical methodologies were used to compare four different outcomes: death, admission into an intensive care unit (ICU), development of severe/critical symptoms, and utilization of invasive mechanical ventilation; between patients with cancer (of different types, stages, and treatments of cancer) and patients without cancer.Findings: Compared with COVID-19 patients without cancer, COVID-19 patients with cancer had higher risks in all four severe outcomes. Patients with blood cancers, lung cancers, or with metastatic cancer (stage IV) had the highest frequency of severe events. Non-metastatic cancer (stage I-III) patients experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received immunotherapy and surgery had higher risks of having severe events, while patients with only radiotherapy and targeted therapy did not demonstrate significant differences in severe events when compared to patients without cancer.Interpretations: Patients with blood cancer, lung cancer, and metastatic cancer demonstrated a higher incidence of severe events compared to patients without cancer. In addition, patients who underwent immunotherapy or cancer surgery had higher death rates and higher chances of having critical symptoms.},
	author = {Meng-Yuan Dai and Dianbo Liu and Liu, Miao and Fu-Xiang Zhou and .... and Lorelei Ann Mucci and Santillana, Mauricio and Hong-Bing Cai}
}
@article {651628,
	title = {Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China},
	journal = {Nature },
	volume = {https://doi.org/10.1038/s41586-020-2293-x},
	year = {2020},
	abstract = {Background: The COVID-19 outbreak containment strategies in China based on non-pharmaceutical interventions (NPIs) appear to be effective. Quantitative research is still needed however to assess the efficacy of different candidate NPIs and their timings to guide ongoing and future responses to epidemics of this emerging disease across the World. Methods: We built a travel network-based susceptible-exposed-infectious-removed (SEIR) model to simulate the outbreak across cities in mainland China. We used epidemiological parameters estimated for the early stage of outbreak in Wuhan to parameterise the transmission before NPIs were implemented. To quantify the relative effect of various NPIs, daily changes of delay from illness onset to the first reported case in each county were used as a proxy for the improvement of case identification and isolation across the outbreak. Historical and near-real time human movement data, obtained from Baidu location-based service, were used to derive the intensity of travel restrictions and contact reductions across China. The model and outputs were validated using daily reported case numbers, with a series of sensitivity analyses conducted. Results: We estimated that there were a total of 114,325 COVID-19 cases (interquartile range [IQR] 76,776 - 164,576) in mainland China as of February 29, 2020, and these were highly correlated (p\&lt;0.001, R2=0.86) with reported incidence. Without NPIs, the number of COVID-19 cases would likely have shown a 67-fold increase (IQR: 44 - 94), with the effectiveness of different interventions varying. The early detection and isolation of cases was estimated to prevent more infections than travel restrictions and contact reductions, but integrated NPIs would achieve the strongest and most rapid effect. If NPIs could have been conducted one week, two weeks, or three weeks earlier in China, cases could have been reduced by 66\%, 86\%, and 95\%, respectively, together with significantly reducing the number of affected areas. However, if NPIs were conducted one week, two weeks, or three weeks later, the number of cases could have shown a 3-fold, 7-fold, and 18-fold increase across China, respectively. Results also suggest that the social distancing intervention should be continued for the next few months in China to prevent case numbers increasing again after travel restrictions were lifted on February 17, 2020. Conclusion: The NPIs deployed in China appear to be effectively containing the COVID-19 outbreak, but the efficacy of the different interventions varied, with the early case detection and contact reduction being the most effective. Moreover, deploying the NPIs early is also important to prevent further spread. Early and integrated NPI strategies should be prepared, adopted and adjusted to minimize health, social and economic impacts in affected regions around the World.},
	url = {https://doi.org/10.1038/s41586-020-2293-x},
	author = {Shengjie Lai and Nick W Ruktanonchai and Liangcai Zhou and Olivia Prosper and Wei Luo and Jessica R Floyd and Amy Wesolowski and Santillana, Mauricio and Zhang, Chi and Xiangjun Du and Hongjie Yu and Tatem, Andrew J}
}
@article {651627,
	title = {COVID-19 Positive Cases, Evidence on the Time Evolution of the Epidemic or An Indicator of Local Testing Capabilities? A Case Study in the United States},
	journal = {SSRN},
	year = {2020},
	abstract = {The novel SARS-CoV-2 coronavirus, first identified in Wuhan (Hubei), China, in December 2019, has spread to more than 180 countries and caused over 1,700,000 cases of COVID-19 worldwide to date. In an effort to limit human-to-human contact and slow the transmission of COVID-19, the disease caused by this novel coronavirus, the United States have implemented a collection of shelter-in-place public health interventions. To monitor if these interventions are working and to determine when people may go back to (perhaps a new) business as usual requires reliable monitoring systems that provide an accurate real-time picture of the trajectory of the epidemic outbreak. Here, we present evidence that our current healthcare-based monitoring systems, aimed at detecting the new daily number of COVID-19-positive individuals across the US, may be better at tracking the local testing (detection) capabilities than at monitoring the time evolution of the outbreak. This suggests that other data sources are necessary to inform (real-time) critical decisions about when to stop (and perhaps when to restart) shelter-in-place mitigation strategies.},
	author = {Justin Kaashoek and Santillana, Mauricio}
}
@article {651626,
	title = {The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales.},
	journal = {Scientific Reports},
	volume = {10},
	year = {2020},
	pages = {17002},
	abstract = {A novel coronavirus (SARS-CoV-2) was identified in Wuhan, Hubei Province, China, in December 2019 and has caused over 240,000 cases of COVID-19 worldwide as of March 19, 2020. Previous studies have supported an epidemiological hypothesis that cold and dry environments facilitate the survival and spread of droplet-mediated viral diseases, and warm and humid environments see attenuated viral transmission (e.g., influenza). However, the role of temperature and humidity in transmission of COVID-19 has not yet been established. Here, we examine the spatial variability of the basic reproductive numbers of COVID-19 across provinces and cities in China and show that environmental variables alone cannot explain this variability. Our findings suggest that changes in weather alone (i.e., increase of temperature and humidity as spring and summer months arrive in the Northern Hemisphere) will not necessarily lead to declines in case count without the implementation of extensive public health interventions.},
	author = {Canelle Poirier and Wei Luo and Maimuna S. Majumder and Dianbo Liu and Mandl, Kenneth and Todd Mooring and Santillana, Mauricio}
}
@article {651625,
	title = {A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models},
	journal = {Journal of Medical Internet Research},
	volume = {22},
	number = {8},
	year = {2020},
	abstract = {We present a timely and novel methodology that combines disease estimates from mechanistic models with digital traces, via interpretable machine-learning methodologies, to reliably forecast COVID-19 activity in Chinese provinces in real-time. Specifically, our method is able to produce stable and accurate forecasts 2 days ahead of current time, and uses as inputs (a) official health reports from Chinese Center Disease for Control and Prevention (China CDC), (b) COVID-19-related internet search activity from Baidu, (c) news media activity reported by Media Cloud, and (d) daily forecasts of COVID-19 activity from GLEAM, an agent-based mechanistic model. Our machine-learning methodology uses a clustering technique that enables the exploitation of geo-spatial synchronicities of COVID-19 activity across Chinese provinces, and a data augmentation technique to deal with the small number of historical disease activity observations, characteristic of emerging outbreaks. Our model{\textquoteright}s predictive power outperforms a collection of baseline models in 27 out of the 32 Chinese provinces, and could be easily extended to other geographies currently affected by the COVID-19 outbreak to help decision makers.},
	author = {Dianbo Liu and Clemente, Leonardo and Canelle Poirier and Xiyu Ding and Matteo Chinazzi and Jessica T Davis and Alessandro Vespignani and Santillana, Mauricio}
}
@article {651624,
	title = {Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States},
	journal = {arXiv},
	year = {2020},
	abstract = {In March of 2020, many U.S. state governments encouraged or mandated restrictions on social interactions to slow the spread of COVID-19, the disease caused by the novel coronavirus SARS-CoV-2 that has spread to nearly 180 countries. Estimating the effectiveness of these social-distancing strategies is challenging because surveillance of COVID-19 has been limited, with tests generally being prioritized for high-risk or hospitalized cases according to temporally and regionally varying criteria. Here we show that reductions in mobility across U.S. counties with at least 100 confirmed cases of COVID-19 led to reductions in fever incidences, as captured by smart thermometers, after a mean lag of 6.5 days (90\% within 3--10 days) that is consistent with the incubation period of COVID-19. Furthermore, counties with larger decreases in mobility subsequently achieved greater reductions in fevers (p\&lt;0.01), with the notable exception of New York City and its immediate vicinity. These results indicate that social distancing has reduced the transmission of influenza like illnesses, including COVID 19, and support social distancing as an effective strategy for slowing the spread of COVID-19.},
	author = {Parker Liautaud and Huybers, Peter and Santillana, Mauricio}
}
@article {648300,
	title = {The role of absolute humidity on transmission rates of the COVID-19 outbreak.},
	journal = {medRxiv },
	year = {2020},
	abstract = {A novel coronavirus (COVID-19) was identified in Wuhan, Hubei Province, China, in December 2019 and has caused over 40,000 cases worldwide to date. Previous studies have supported an epidemiological hypothesis that cold and dry (low absolute humidity) environments facilitate the survival and spread of droplet-mediated viral diseases, and warm and humid (high absolute humidity) environments see attenuated viral transmission (i.e., influenza). However, the role of absolute humidity in transmission of COVID-19 has not yet been established. Here, we examine province-level variability of the basic reproductive numbers of COVID-19 across China and find that changes in weather alone (i.e., increase of temperature and humidity as spring and summer months arrive in the North Hemisphere) will not necessarily lead to declines in COVID-19 case counts without the implementation of extensive public health interventions.},
	author = {Luo, W and M S Majumder and D Liu and C Poirier and Mandl, K. and M. Lipsitch and Santillana, Mauricio}
}
@article {648298,
	title = {Fitbit-informed influenza forecasts},
	journal = {Lancet Digital Health},
	volume = {2},
	number = {2},
	year = {2020},
	author = {Cecile Viboud and Santillana, Mauricio}
}
@article {643997,
	title = {Real-time Estimation of Disease Activity in EmergingOutbreaks using Internet Search Information},
	journal = {PLoS Computational Biology},
	year = {2020},
	abstract = {Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel Internet-based data sources, such as disease-related Internet search activity, can produce meaningful "nowcasts" of disease incidence ahead of healthcare-based estimates, with most successful case studies focusing on endemic and seasonal diseases such as influenza and dengue. Here, we apply similar computational methods to emerging outbreaks in geographic regions where no historical presence of the disease of interest has been observed. By combining limited available historical epidemiological data available with disease-related Internet search activity, we retrospectively estimate disease activity in five recent outbreaks weeks ahead of traditional surveillance methods. We find that the proposed computational methods frequently provide useful real-time incidence estimates that can help fill temporal data gaps resulting from surveillance reporting delays. How- ever, the proposed methods are limited by issues of sample bias and skew in search query volumes, perhaps as a result of media coverage.},
	author = {Emily Aiken and Sarah McGough and Maia Majumder and Gal Wachtel and Andre T Nguyen and Cecile Viboud and Santillana, Mauricio}
}
@article {641995,
	title = {An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models},
	journal = {Geoscientific Model Development },
	volume = {13},
	year = {2020},
	pages = {2475{\textendash}2486},
	abstract = {Abstract. The major computational bottleneck in atmospheric chemistry models is the numerical integration of the stiff coupled system of kinetic equations describing the chemical evolution of the system as defined by the model chemical mechanism (typically over 100 coupled species). We present an adaptive method to greatly reduce the computational cost of that numerical integration in global 3-D models while maintaining high accuracy. Most of the atmosphere does not in fact require solving for the full chemical complexity of the mechanism, so considerable simplification is possible if one can recognize the dynamic continuum of chemical complexity required across the atmospheric domain. We do this by constructing a limited set of reduced chemical mechanisms (chemical regimes) to cover the range of atmospheric conditions, and then pick locally and on the fly which mechanism to use for a given gridbox and time step on the basis of computed production and loss rates for individual species. Application to the GEOS-Chem global 3-D model for oxidant-aerosol chemistry in the troposphere and stratosphere (full mechanism of 228 species) is presented. We show that 20 chemical regimes can largely encompass the range of conditions encountered in the model. Results from a 2-year GEOS-Chem simulation shows that our method can reduce the computational cost of chemical integration by 30-40\% while maintaining accuracy better than 1\% and with no error growth. Our method retains the full complexity of the original chemical mechanism where it is needed, provides the same model output diagnostics (species production and loss rates, reaction rates) as the full mechanism, and can accommodate changes in the chemical mechanism or in model resolution without having to reconstruct the chemical regimes.},
	author = {Shen, Lu and Daniel J. Jacob and Santillana, Mauricio and Xuan Wang and Chen, Wei}
}
@article {636269,
	title = {Adding Continuous Vital-Sign Information to Static Clinical Data Improves Prediction of Length of Stay Following Intubation. A Data Driven Machine Learning Approach},
	journal = {Respiratory Care},
	volume = {65},
	number = {9},
	year = {2020},
	abstract = {
	Background. Bedside monitors in intensive care units (ICUs) routinely measure and collect patients{\textquoteright} physiologic data in real time in order to continuously assess the health status of critically ill patients. With the advent of increased computational power and the ability to store and rapidly process big datasets in recent years, this physiologic data show promise in identifying specific outcomes and/or events during patients{\textquoteright} ICU hospitalization. \&nbsp;Methods. We introduce a methodological workflow capable of automatically extracting meaningful information from continuous-in-time vital signs to predict patient outcomes. Our prediction algorithms are based on robust and scalable machine-learning techniques. We demonstrate the feasibility and applicability of this approach to a pilot study aimed at predicting whether or not a patient will experience a prolonged ICU length of stay (defined as longer than 4 days) in a cohort of 284 mechanically ventilated patients collected from a pediatric medical and surgical ICU at Boston Children{\textquoteright}s Hospital, using only information collected or available during the first 24 hours of mechanical ventilation.



	Results. Our methodology achieves predictive accuracies above 83\% (AUC) by using only vital sign information collected from bedside physiologic monitors. In addition, we show that combining vital sign information with patients{\textquoteright} demographic and clinical history data contained in electronic health records, improves the accuracy of our approach to accuracies of 90\% (AUC). The predictive power of our methodology is assessed strictly on an unseen hold-out validation set of patients.



	Conclusions. Our proposed workflow may prove useful in the design of scalable approaches for real-time predictive systems in ICU environments exploiting real time vital sign information from bedside monitors.

},
	author = {D Castiñeira and K Schlosser and A Geva and A Rahmani and G Fiore and B K Walsh and C D Smallwood and J H Arnold and Santillana, M}
}
@article {622873,
	title = {Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000-2016},
	journal = {Eurosurveillance},
	volume = {25},
	number = {45},
	year = {2020},
	pages = {1900414},
	abstract = {
	Background. Widely recognized as a major public health threat globally, the rapid increase of antibiotic resistance in bacteria could soon render our most effective method to combat infections obsolete. Factors influencing the burden of resistance in human populations remain poorly described, though temperature is known to play an important role in mechanisms at the bacterial level.



	Methods. We performed an ecologic analysis of country level antibiotic resistance prevalence in 3 common bacterial pathogens across 28 countries in Europe, and used multivariable models to evaluate associations with minimum temperature and other predictors over a 17-year period (2000-2016). We quantified the effects of minimum temperature, population density, and antibiotic consumption on the rate of change of antibiotic resistance across geographies.



	Findings. For three common bacterial pathogens and four classes of antibiotics, we found evidence of a long-term effect of ambient minimum temperature on rates of increase of antibiotic resistance across 28 countries in Europe between 2000-2016. Specifically, we show that across all antibiotic classes for the pathogens E. coli and K. pneumoniae, European countries with 10{\textdegree}C warmer ambient temperatures have experienced more rapid increases in antibiotic resistance over the 17-year period, ranging between 0.33\%/year (95\% CI 0.2, 0.5) and 1.2\%/year (0.4, 1.9), even after accounting for recognized drivers of resistance including antibiotic consumption and population density. We found a decreasing relationship for S. aureus and methicillin of -0.4\%/year (95\% CI -0.7, 0.0), reflecting widespread declines in MRSA across Europe over the study period.



	Interpretation. Ambient temperature may be an important modulator of the rate of change of antibiotic resistance. Our findings suggest that rising temperatures globally may hasten the spread of resistance and complicate efforts to mitigate it.



	\&nbsp;

},
	author = {Sarah McGough and Derek R. MacFadden and Mohammad W Hattab and K{\r a}re M{\o}lbak and Santillana, Mauricio}
}
@article {640037,
	title = {Leveraging Google Search Data to Track Influenza Outbreaks in Africa},
	journal = {Gates Open Research},
	volume = {3},
	year = {2019},
	pages = {1653},
	abstract = {Background: Traditionally, public health agencies track seasonal influenza activity by collecting information from clinics,hospitals, and laboratories. \&nbsp;The inherent slowness of the collection processes of influenza activity data limits the ability ofpublic health agencies to adapt to unexpected changes in influenza activity in near real-time. In recent years, new influenzasurveillance methods that use nontraditional data sources, such as Google searches, have been proposed to successfullyestimate influenza activity in near real-time. \&nbsp;However, most of these methods have been designed and implemented inhigh-income countries even though influenza disease burden remains high in low- to middle-income countries.Objective: We seek to predict influenza activity in near real-time in Africa using machine learning models that combine Googlesearches with traditional epidemiological data.Methods:We extend the AutoRegression with General Online data (ARGO) model, which was originally designed to trackinfluenza activity in the United States, to combine influenza-related Google searches with historical laboratory-confirmedinfluenza trends in Africa in near real-time. We evaluate the predictive performance of the ARGO model and compare it withseveral benchmark models in Algeria, Ghana, Morocco, and South Africa. We explore the advantages and limitations of usingGoogle search data to monitor influenza activity.Results: In South Africa, Algeria, and Morocco, the ARGO model outperforms all benchmark models, suggesting that in thesecountries, incorporating influenza-related Google search information in predictive models leads to improved predictions. InGhana, however, the ARGO model and the autoregressive model of historical influenza activity have comparable performances.These results demonstrate that the quality of the ARGO predictions is higher in regions where: (a) influenza activity is seasonal,(b) historical influenza activity is recorded consistently, and (c) the volume of influenza-related Google search queries is enoughto appear as non-zero in the Google Trends tool.Keywords: Real-time disease surveillance, Digital epidemiology, Google Flu Trends, Influenza monitoring, Seasonal influenza},
	author = {Karla Mejia and Cecile Viboud and Santillana, Mauricio}
}
@inbook {637427,
	title = {Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic},
	booktitle = {Global Catastrophic Biological Risk},
	year = {2019},
	publisher = {Current Topics in Microbiology and Immunology. Springer, Berlin, Heidelberg.},
	organization = {Current Topics in Microbiology and Immunology. Springer, Berlin, Heidelberg.},
	abstract = {Catastrophic epidemics, if they occur, will very likely start from localized and far smaller (non-catastrophic) outbreaks that grow into much greater threats. One key bulwark against this outcome is the ability of governments and the health sector more generally to make informed decisions about control measures based on accurate understanding of the current and future extent of the outbreak.Situation reporting is the activity of periodically summarizing the state of the outbreak in a (usually) public way. We delineate key classes of decisions whose quality depends on high-quality situation reporting, key quantities for which estimates are needed to inform these decisions, and the traditional and novel sources of data that can aid in estimating these quantities. We emphasize the important role of situation reports as providing public, shared planning assumptions that allow decision makers to harmonize the response while making explicit the uncertainties that underlie the scenarios outlined for planning. In this era of multiple data sources and complex factors informing the interpretation of these data sources, we describe four principles for situation reporting: (1) Situation reporting should be thematic, concentrating on essential areas of evidence needed for decisions. (2) Situation reports should adduce evidence from multiple sources to address each area of evidence, along with expert assessments of key parameters. (3) Situation reports should acknowledge uncertainty and attempt to estimate its magnitude for each assessment. (4) Situation reports should contain carefully curated visualizations along with text and tables.},
	url = {https://link.springer.com/chapter/10.1007/82_2019_172},
	author = {Marc Lipsitch and Santillana, Mauricio},
	editor = {Tom Inglesby}
}
@article {630882,
	title = {Chikungunya virus outbreak in the Amazon region: replacement of the Asian genotype by an ECSA lineage},
	journal = {PLoS Neglected Tropical Diseases},
	volume = {13},
	number = {3},
	year = {2019},
	pages = {e0007065},
	author = {F G Naveca and I Claro and M Giovanetti and J G Jesus and J Javier and F C M Iani and V A do Nascimento and V C Souza and P P Silveira and J Lourenco and Santillana, M and M Kraemer and N R Faria}
}
@article {629679,
	title = {Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation},
	journal = {JMIR Public Health Surveillance},
	volume = {5},
	number = {4},
	year = {2019},
	pages = {e13403},
	abstract = {Background: The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear.Objective: The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources{\textemdash}CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org{\textemdash}to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet{\textquoteright}s data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas.Methods: We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care{\textendash}seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care{\textendash}seeking percentages and baselines for each surveillance data source.Results: We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines.Conclusions: Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models.},
	author = {Baltrusaitis, Kristin and Alessandro Vespignani and Roni Rosenfeld and Gray, Josh and Dorrie Raymond and Santillana, Mauricio}
}
@article {629678,
	title = {Noninvasive Ventilation Is Interrupted Frequently and Mostly Used at Night in the Pediatric Intensive Care Unit},
	journal = {Respiratory Care},
	volume = {64},
	number = {9},
	year = {2019},
	abstract = {Background: Noninvasive ventilation is commonly used to support children with respiratory failure, but detailed patterns of real-world use are lacking. The aim of our study was to describe use patterns of noninvasive ventilation (NIV) using electronic medical record (EMR) data.Methods: We performed a retrospective electronic chart review in a tertiary care PICU in the United States. Subjects admitted to the PICU from 2014-2017 who were mechanically ventilated were included in the study.Results: The median number of discrete device episodes, defined as a time on support without interruption, was 20 (IQR 8-49) per subject. The median duration of bilevel positive airway pressure (BPAP) support prior to interruption was 6.3 hours (IQR 2.4-10.4) and for CPAP was 6 hours (IQR 2.1-10.4). The interruptions to BPAP have a median duration of 6.3 hours (IQR 2-15.5) and interruptions to CPAP 8.6 hours (IQR 2.2-16.8). Use of NIV followed a diurnal pattern, with 44 percent of BPAP and 42 percent of CPAP subjects initiating support between 7 p.m. and midnight and 49 percent of BPAP and 46 percent of CPAP subjects stopping support between 5 a.m. and 10 a.m.Conclusions: NIV is frequently interrupted, and initiation and discontinuation of NIV follows a diurnal pattern. Use of EMR data collected for routine clinical care allow granular details of typical use patterns to be analyzed. Understanding NIV use patterns may be particularly important to understanding the burden of PICU bed utilization for nocturnal NIV. To our knowledge this is the first study to examine in detail the use of pediatric NIV and define diurnal use and frequent interruptions to support. \&nbsp;},
	author = {Katherine R. Schlosser and Gaston A. Fiore and Craig D. Smallwood and John Griffin and Geva, Alon and Santillana, Mauricio and Arnold, John H.}
}
@article {622872,
	title = {Improved real-time influenza surveillance using Internet search data in eight Latin American countries},
	journal = {JMIR Public Health Surveillance},
	volume = {5},
	number = {2},
	year = {2019},
	pages = {e12214},
	abstract = {Background: Novel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates.Objective: The aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America.Methods: A machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information.Results: Our results show that ARGO-like models{\textquoteright} predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available.Conclusions: We demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates.},
	author = {Leonardo C Clemente and Lu, Fred and Santillana, Mauricio}
}
@article {611951,
	title = {Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet},
	journal = {Nature Communications},
	volume = {10},
	number = {147},
	year = {2019},
	author = {Lu, Fred and Hattab, Mohammad and Clemente, Leonardo and Santillana, Mauricio}
}
@article {606100,
	title = {Internet search query data improves forecasts of daily emergency department volume},
	journal = {Journal of the American Medical Informatics Association},
	volume = {ocz154},
	year = {2019},
	author = {S Tideman and Santillana, M and Bickel, J. and Reis, B.}
}
@article {625486,
	title = {Estimation of Pneumonic Plague Transmission in Madagascar, August{\textendash}November 2017},
	journal = {PLOS Currents Outbreaks},
	volume = {1},
	year = {2018},
	author = {Majumder, Maia S. and Cohn, Emily L and Santillana, Mauricio and J. S. Brownstein}
}
@article {622761,
	title = {Comparison of crowd-sourced, electronic health records based, and traditional health-care based influenza-tracking systems at multiple spatial resolutions in the United States of America},
	journal = {BMC infectious diseases},
	volume = {18},
	number = {403},
	year = {2018},
	author = {K. Baltrusaitis and J. Brownstein and S. Scarpino and E. Bakota and A. Crawley and J. Conidi and J. Gunn and J. Gray and Zink, A. and Santillana, M.}
}
@article {572846,
	title = {Accurate influenza monitoring and forecasting in the Boston metropolis using novel Internet data streams.},
	journal = {Journal of Medical Internet Research},
	volume = {4},
	number = {1},
	year = {2018},
	pages = {e4},
	author = {F. Lu and S Hou and K Baltrusaitis and M. Shah and J. Leskovec and R. Sosic and J. Hawkins and J. S. Brownstein and G. Conidi and J. Gunn and J. Gray and Zink, A. and Santillana, M.}
}
@article {572841,
	title = {Antibiotic Resistance Increases with Local Temperature.},
	journal = {Nature Climate Change},
	volume = {8},
	year = {2018},
	pages = {510{\textendash}514},
	author = {D. R. MacFadden and S.F. McGough and D. Fisman and Santillana, M. and J. S. Brownstein}
}
@article {santillana2016relatedness,
	title = {Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model," Farr{\textquoteright}s Law" and SIR Compartmental Difference Equation Models},
	journal = {Infectious Disease Modelling},
	volume = {3},
	year = {2018},
	pages = {1-12},
	author = {Santillana, Mauricio and Tuite, Ashleigh and Nasserie, Tahmina and Fine, Paul and Champredon, David and Chindelevitch, Leonid and Dushoff, Jonathan and Fisman, David}
}
@article {572836,
	title = {Combining Participatory Influenza Surveillance with Modeling and Forecasting},
	journal = {Journal of Medical Internet Research Public Health Surveillance},
	volume = {3},
	number = {4},
	year = {2017},
	pages = {e83},
	author = {A. Marathe and J. S. Brownstein and S. Chu and M. V. Marathe and A. T. Nguyen and D. Paolotti and N. Perra and D. Perrotta and Santillana, M. and S. Swarup and M. Tizzoni and A. Vespignani and A. K. S. Vullikanti and M. L. Wilson and Q. Zhang}
}
@article {572771,
	title = {County-level assessment of United States kindergarten vaccination rates for measles mumps rubella (MMR) for the 2014--2015 school year},
	journal = {Vaccine},
	volume = {35},
	year = {2017},
	pages = {6444-6450},
	author = {S. A. Kluberg and D. P. McGinnis and Y. Hswen and M. S. Majumder and Santillana, M. and J. S. Brownstein}
}
@article {520841,
	title = {Determinants of Participants{\textquoteright} Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveilla},
	journal = {JMIR Public Health Surveillance},
	volume = {3},
	number = {3},
	year = {2017},
	pages = {e18},
	author = {Baltrusaitis, Kristin and Santillana, Mauricio and Adam Crawley and Chunara, R. and Mark Smolinski and John Brownstein}
}
@article {479856,
	title = {Advances in the use of Google searches to track dengue in Mexico, Brazil, Thailand, Singapore and Taiwan},
	journal = {PLoS Computational Biology},
	volume = {13},
	number = {7},
	year = {2017},
	month = {Jul, 2017},
	pages = {e1005607},
	author = {Yang, S. and S. C. Kou and F. Lu and J. S. Brownstein and N. Brooke and Santillana, M}
}
@article {479866,
	title = {Using electronic health records and Internet search information for enhanced influenza forecast},
	journal = {BMC infectious diseases},
	volume = {17},
	number = {1},
	year = {2017},
	month = {May, 2017},
	pages = {332},
	author = {Yang, S. and Santillana, M. and J. S. Brownstein and J. Gray and S. Richardson and S. C Kou}
}
@article {456536,
	title = {Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data.},
	journal = {PLoS Neglected Tropical Diseases},
	volume = {11},
	number = {1},
	year = {2017},
	pages = {e0005295},
	author = {S.F. McGough and J. S. Brownstein and J. Hawkins and Santillana, M.}
}
@article {456531,
	title = {Perspectives on the future of Internet search engines and biosurveillance systems.},
	journal = {Clinical Infectious Diseases},
	volume = {64},
	number = {1},
	year = {2017},
	month = {Sep 2016},
	pages = {42-43},
	author = {Santillana, M.}
}
@article {456526,
	title = {Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico},
	journal = {Scientific Reports},
	volume = {6},
	number = {33707},
	year = {2016},
	author = {M.A. Johansson and N.G. Reich and A. Hota and J. S. Brownstein and Santillana, M.}
}
@article {santillana2016cloud,
	title = {Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance},
	journal = {Scientific reports},
	volume = {6},
	year = {2016},
	publisher = {Nature Publishing Group},
	author = {Santillana, Mauricio and Nguyen, AT and Louie, Tamara and Zink, Anna and Gray, Josh and Sung, Iyue and Brownstein, John S}
}
@article {majumder2016utilizing,
	title = {Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak},
	journal = {JMIR public health and surveillance},
	volume = {2},
	number = {1},
	year = {2016},
	pages = {e30},
	publisher = {JMIR Publications Inc., Toronto, Canada},
	author = {Majumder, Maimuna S and Santillana, Mauricio and Mekaru, Sumiko R and McGinnis, Denise P and Khan, Kamran and Brownstein, John S}
}
@article {santillana2016estimating,
	title = {Estimating numerical errors due to operator splitting in global atmospheric chemistry models: Transport and chemistry},
	journal = {Journal of Computational Physics},
	volume = {305},
	year = {2016},
	pages = {372{\textendash}386},
	publisher = {Academic Press},
	author = {Santillana, Mauricio and Zhang, Lin and Yantosca, Robert}
}
@article {majumder20152014,
	title = {2014 Ebola outbreak: Media events track changes in observed reproductive number},
	journal = {PLoS Currents Outbreaks},
	year = {2015},
	publisher = {Public Library of Science},
	author = {Majumder, Maimuna S and Kluberg, Sheryl and Santillana, Mauricio and Mekaru, Sumiko and Brownstein, John S}
}
@article {smolinski2015flu,
	title = {Flu Near You: crowdsourced symptom reporting spanning 2 influenza seasons},
	journal = {American journal of public health},
	volume = {105},
	number = {10},
	year = {2015},
	pages = {2124{\textendash}2130},
	publisher = {American Public Health Association},
	author = {Smolinski, Mark S and Crawley, Adam W and Baltrusaitis, Kristin and Chunara, Rumi and Olsen, Jennifer M and W{\'o}jcik, Oktawia and Santillana, Mauricio and Nguyen, Andre and Brownstein, John S}
}
@article {yang2015accurate,
	title = {Accurate estimation of influenza epidemics using Google search data via ARGO},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {112},
	number = {47},
	year = {2015},
	pages = {14473{\textendash}14478},
	publisher = {National Acad Sciences},
	author = {Yang, Shihao and Santillana, Mauricio and Kou, SC}
}
@article {santillana2015combining,
	title = {Combining search, social media, and traditional data sources to improve influenza surveillance},
	journal = {PLoS Comput Biol},
	volume = {11},
	number = {10},
	year = {2015},
	pages = {e1004513},
	publisher = {Public Library of Science},
	author = {Santillana, Mauricio and Nguyen, Andr{\'e} T and Dredze, Mark and Paul, Michael J and Nsoesie, Elaine O and Brownstein, John S}
}
@article {nagar2014case,
	title = {A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives},
	journal = {Journal of medical Internet research},
	volume = {16},
	number = {10},
	year = {2014},
	pages = {e236},
	publisher = {JMIR Publications Inc., Toronto, Canada},
	author = {Nagar, Ruchit and Yuan, Qingyu and Freifeld, Clark C and Santillana, Mauricio and Nojima, Aaron and Chunara, Rumi and Brownstein, John S}
}
@article {gluskin2014evaluation,
	title = {Evaluation of Internet-based dengue query data: Google Dengue Trends},
	journal = {PLoS Negl Trop Dis},
	volume = {8},
	number = {2},
	year = {2014},
	pages = {e2713},
	publisher = {Public Library of Science},
	author = {Gluskin, Rebecca Tave and Johansson, Michael A and Santillana, Mauricio and Brownstein, John S}
}
@article {santillana2014using,
	title = {Using clinicians{\textquoteright} search query data to monitor influenza epidemics},
	journal = {Clinical Infectious Diseases},
	volume = {59},
	number = {10},
	year = {2014},
	pages = {1446{\textendash}1450},
	publisher = {Oxford University Press},
	author = {Santillana, Mauricio and Nsoesie, Elaine O and Mekaru, Sumiko R and Scales, David and Brownstein, John S}
}
@article {santillana2014can,
	title = {What can digital disease detection learn from (an external revision to) Google Flu Trends?},
	journal = {American journal of preventive medicine},
	volume = {47},
	number = {3},
	year = {2014},
	pages = {341{\textendash}347},
	publisher = {Elsevier},
	author = {Santillana, Mauricio and Zhang, D Wendong and Althouse, Benjamin M and Ayers, John W}
}
@article {santillana2013quantifying,
	title = {Quantifying the loss of information in source attribution problems using the adjoint method in global models of atmospheric chemical transport},
	journal = {arXiv preprint arXiv:1311.6315},
	year = {2013},
	author = {Santillana, Mauricio}
}
@article {calo2013gradient,
	title = {Gradient-based estimation of Manning{\textquoteright}s friction coefficient from noisy data},
	journal = {Journal of Computational and Applied Mathematics},
	volume = {238},
	year = {2013},
	pages = {1{\textendash}13},
	publisher = {North-Holland},
	author = {Calo, Victor M and Collier, Nathan and Gehre, Matthias and Jin, Bangti and Radwan, Hany and Santillana, Mauricio}
}
@article {radwan2012convergence,
	title = {Convergence rates for diffusive shallow water equations (DSW) using higher order polynomials},
	journal = {Journal of the Serbian Society for Computational Mechanics/Vol},
	volume = {6},
	number = {1},
	year = {2012},
	pages = {160{\textendash}168},
	author = {Radwan, HG and Vignal, P and Collier, N and Dalcin, L and Santillana, M and Calo, VM}
}
@article {santillana2010adaptive,
	title = {An adaptive reduction algorithm for efficient chemical calculations in global atmospheric chemistry models},
	journal = {Atmospheric Environment},
	volume = {44},
	number = {35},
	year = {2010},
	pages = {4426{\textendash}4431},
	publisher = {Elsevier},
	author = {Santillana, Mauricio and Le Sager, Philippe and Daniel J. Jacob and Brenner, Michael P}
}
@article {zhan2010estimating,
	title = {Estimating small-area population growth using geographic-knowledge-guided cellular automata},
	journal = {International Journal of Remote Sensing},
	volume = {31},
	number = {21},
	year = {2010},
	pages = {5689{\textendash}5707},
	publisher = {Taylor \& Francis},
	author = {Zhan, F Benjamin and Tapia Silva, Felipe Omar and Santillana, Mauricio}
}
@article {santillana2010local,
	title = {A local discontinuous Galerkin method for a doubly nonlinear diffusion equation arising in shallow water modeling},
	journal = {Computer Methods in Applied Mechanics and Engineering},
	volume = {199},
	number = {23},
	year = {2010},
	pages = {1424{\textendash}1436},
	publisher = {North-Holland},
	author = {Santillana, Mauricio and Dawson, Clint}
}
@article {santillana2010numerical,
	title = {A numerical approach to study the properties of solutions of the diffusive wave approximation of the shallow water equations},
	journal = {Computational Geosciences},
	volume = {14},
	number = {1},
	year = {2010},
	pages = {31{\textendash}53},
	publisher = {Springer},
	author = {Santillana, Mauricio and Dawson, Clint}
}
@book {santillana2008analysis,
	title = {Analysis and numerical simulation of the diffusive wave approximation of the shallow water equations},
	year = {2008},
	publisher = {ProQuest},
	organization = {ProQuest},
	author = {Santillana, Mauricio}
}
@article {alonso2008diffusive,
	title = {On the diffusive wave approximation of the shallow water equations},
	journal = {European Journal of Applied Mathematics},
	volume = {19},
	number = {05},
	year = {2008},
	pages = {575{\textendash}606},
	publisher = {Cambridge University Press},
	author = {Alonso, R and Santillana, Mauricio and Dawson, Clint}
}
