%0 Journal Article
%J medRxiv preprint doi: ttps://doi.org/10.1101/2024.02.21.24303099
%D 2024
%T 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
%A Tigist F. Menkir
%A Citarella, Barbara Wanjiru
%A Sigfrid, Louise
%A Yash Doshi
%A Reyes, Luis Felipe
%A Jose A. Calvache
%A Anders Benjamin Kildal
%A Anders B. Nygaard
%A Jan Cato Holter
%A Panda, Prasan Kumar
%A Jassat, Waasila
%A Merson, Laura
%A Christl A. Donnelly
%A Santillana, Mauricio
%A Buckee, Caroline
%A Verguet, Stéphane
%A Nima S. Hejazi
%X 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’ 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.
%B medRxiv preprint doi: ttps://doi.org/10.1101/2024.02.21.24303099
%G eng
%U https://www.medrxiv.org/content/10.1101/2024.02.21.24303099v1
%0 Journal Article
%J JAMA Netw
%D 2024
%T Cognitive Symptoms of Post–COVID-19 Condition and Daily Functioning
%A Abhishek Jaywant
%A Faith M. Gunning
%A Lauren E. Oberlin
%A Santillana, Mauricio
%A Katherine Ognyanova
%A ames N. Druckman
%A Baum, Matthew A.
%A Lazer, David
%A Roy H. Perlis
%X Importance The frequent occurrence of cognitive symptoms in post–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 To investigate the prevalence of self-reported cognitive symptoms in post–COVID-19 condition, in comparison with individuals with prior acute SARS-CoV-2 infection who did not develop post–COVID-19 condition, and their association with other individual features, including depressive symptoms and functional status.
Design, Setting, and Participants 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 Post–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 Seven items from the Neuro-QoL cognition battery assessing the frequency of cognitive symptoms in the past week and patient Health Questionnaire-9.
Results 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–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–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 The findings of this survey study of US adults suggest that cognitive symptoms are common among individuals with post–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–COVID-19 condition.
%B JAMA Netw
%G eng
%U https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2815067?utm_campaign=articlePDF&utm_medium=articlePDFlink&utm_source=articlePDF&utm_content=jamanetworkopen.2023.56098
%0 Journal Article
%J medRxiv
%D 2024
%T Mathematical assessment of the role of human behavior changes on SARS-CoV-2 transmission dynamics
%A Binod Pant
%A Salman Safdar
%A Santillana, Mauricio
%A Abba B. Gumel
%X
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 control reproduction number (denoted by RC ) 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 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 r 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 r 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).
%B medRxiv %G eng %U https://www.medrxiv.org/content/10.1101/2024.02.11.24302662v1.full.pdf %0 Journal Article %J medRxiv %D 2024 %T Fine-Grained Forecasting of COVID-19 Trends at the County Level in the United States %A Tzu-Hsi Songa %A Clemente, Leonardo %A Xiang Pana %A Junbong Janga %A Mauricio Santillanab %A Kwonmoo Leea %X 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’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. %B medRxiv %G eng %U https://www.medrxiv.org/content/10.1101/2024.01.13.24301248v1 %0 Journal Article %J Journal of Medical Internet Research %D 2024 %T Pediatric and Young Adult Household Transmission of the Initial Waves of SARS-CoV-2 in the United States: Administrative Claims Study %A Ming Kei Chung %A Brian Hart %A Santillana, Mauricio %A Patel, Chirag J %X Background: 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.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'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’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.
%B OSF Preprints %G eng %U https://www.mighte.org/uploads/1/5/5/4/15543620/covid_states_validation_study.pdf %0 Journal Article %J JAMA Netw Open %D 2022 %T Prevalence and Correlates of Long COVID Symptoms Among US Adults %A Roy H. Perlis %A Santillana, Mauricio %A Katherine Ognyanova %A Safarpour, Alauna %A Trujillo, Kristin Lunz %A Simonson, Matthew D. %A Jon Green %A Alexi Quintana %A James Druckman %A Baum, Matthew A. %A Lazer, David %XImportance Persistence of COVID-19 symptoms beyond 2 months, or long COVID, is increasingly recognized as a common sequela of acute infection.
Objectives 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 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 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 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 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.
%B JAMA Netw Open %G eng %U https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2797782 %0 Journal Article %J Clinical Infectious Diseases %D 2022 %T Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of Coronavirus Disease 2019 in Africa %A Kogan, Nicole E %A Shae Gantt %A David Swerdlow %A Cécile Viboud %A Semakula, Muhammed %A Marc Lipsitch %A Santillana, Mauricio %X BackgroundCharacterizing the dynamics of epidemic trajectories is critical to understanding the potential impacts of emerging outbreaks and to designing appropriate mitigation strategies. As the COVID-19 pandemic evolves, however, the emergence of SARS-CoV-2 variants of concern has complicated our ability to assess in real-time the potential effects of imminent outbreaks, such as those presently caused by the Omicron variant. Here, we report that SARS-CoV-2 outbreaks across regions exhibit strain-specific times from onset to peak, specifically for Delta and 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.
%B medRxiv %G eng %U https://www.medrxiv.org/content/10.1101/2022.01.14.22269288v1.full.pdf %0 Journal Article %J British Journal of Political Science %D 2022 %T Using General Messages to Persuade on a Politicized Scientific Issue %A Jon Green %A James N Druckman %A Matthew A Baum %A Lazer, David %A Katherine Ognyanova %A Matthew Simonson %A Jennifer Lin %A Santillana, Mauricio %A Perlis, Roy H %X 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. %B British Journal of Political Science %V 53 %P 698-706 %G eng %U https://www.cambridge.org/core/journals/british-journal-of-political-science/article/abs/using-general-messages-to-persuade-on-a-politicized-scientific-issue/6AE0FF9C739061ED8F1BE379D7B2998A %N 2 %0 Journal Article %D 2021 %T A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles %A Sarah F. McGough %A Clemente, Leonardo %A J. Nathan Kutz %A Santillana, Mauricio %X 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 %V 18 %G eng %U https://royalsocietypublishing.org/doi/epdf/10.1098/rsif.2020.1006 %N 179 %0 Journal Article %J Pediatric Critical Care Medicine %D 2021 %T Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model %A Bhaven B Patel %A Francesca Sperotto %A Mathieu Molina %A Satoshi Kimura %A Marlon I Delgado %A Santillana, Mauricio %A ohn N Kheir %XTo create a machine-learning model identifying potentially avoidable blood draws for serum potassium among pediatric patients following cardiac surgery.
Retrospective cohort study.
Tertiary-care center.
All patients admitted to the cardiac ICU at Boston Children’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.
None.
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’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, 0–1) 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, 1.3–3.7%) was incorrectly predicted as normal when they were abnormal. A median percentage of 0.0% (interquartile range, 0.0–0.4%) critically low or high measurements was incorrectly predicted as normal. A median of 27.2% (interquartile range, 7.8–32.4%) of samples was correctly predicted to be normal and could have been potentially avoided.
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.
%B Pediatric Critical Care Medicine %V 22 %P 400 %G eng %U https://journals.lww.com/pccmjournal/Fulltext/2021/04000/Avoidable_Serum_Potassium_Testing_in_the_Cardiac.6.aspx %N 392 %0 Journal Article %J PLoS Computational Biology %D 2021 %T A nowcasting framework for correcting for reporting delays in malaria surveillance %A Tigist F. Menkir %A Horace Cox %A Canelle Poirier %A Melanie Saul %A Sharon Jones-Weekes † %A Collette Clementson %A Pablo M. de Salazar %A Santillana, Mauricio %A Caroline O. Buckee %X 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. %B PLoS Computational Biology %V 2 %G eng %U https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009570 %N 17 %0 Journal Article %J AMA network open %D 2021 %T Association Between Social Media Use and Self-reported Symptoms of Depression in US Adults %A Roy H. Perlis %A Jon Green %A Matthew Simonson %A Katherine Ognyanova %A Santillana, Mauricio %A ennifer Lin %A Alexi Quintana %A Hanyu Chwe %A James Druckman %A Lazer, David %A Baum, Matthew A. %A John Della Volpe %XImportance Some studies suggest that social media use is associated with risk for depression, particularly among children and young adults.
Objective To characterize the association between self-reported use of individual social media platforms and worsening of depressive symptoms among adults.
Design, Setting, and Participants 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 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 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 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.
%B AMA network open %V 4 %G eng %U https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2786464 %N 11 %0 Journal Article %J Depression and anxiety %D 2021 %T Gender-specificity of resilience in major depressive disorder %A Roy H. Perlis %A Katherine Ognyanova %A Alexi Quintana %A Jon Green %A Santillana, Mauricio %A Jennifer Lin %A James Druckman %A Lazer, David %A Matthew D Simonson %A Matthew A Baum %A Hanyu Chwe %X 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.Background: 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. Method: 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: 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<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: 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.
%B medRxiv %G eng %U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020988/pdf/nihpp-2021.03.26.21254425.pdf %0 Journal Article %J Geosci. Model Dev. Discuss %D 2021 %T 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 %A Shen, Lu %A Daniel J. Jacob %A Santillana, Mauricio %A Kelvin Bates %A Jiawei Zhuang %A Chen, Wei %XAtmospheric 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.
%B Geosci. Model Dev. Discuss %V 12 %G eng %U https://acmg.seas.harvard.edu/sites/projects.iq.harvard.edu/files/acmg/files/shen2021_gmd.pdf %0 Journal Article %J MedRxiv %D 2021 %T Persistence of symptoms up to 10 months following acute COVID-19 illness %A Roy H. Perlis %A Jon Green %A Santillana, Mauricio %A Lazer, David %A Katherine Ognyanova %A Matthew Simonson %A Baum, Matthew A. %A Alexi Quintana %A Hanyu Chwe %A James Druckman %A John Della Volpe %A Jennifer Lin %X 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. %B MedRxiv %G eng %U https://www.medrxiv.org/content/medrxiv/early/2021/03/08/2021.03.07.21253072.full.pdf %0 Journal Article %J ACM Transactions on Management Information Systems (TMIS) %D 2021 %T High-Resolution Spatio-Temporal Model for County-Level COVID-19 Activity in the U.S. %A Shixiang Zhu %A Alexander Bukharin %A Liyan Xie %A Santillana, Mauricio %A Yang, Shihao %A Yao Xie %X 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’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. %B ACM Transactions on Management Information Systems (TMIS) %V 12 %P 1-22 %G eng %U https://dl.acm.org/doi/pdf/10.1145/3468876 %N 4 %0 Journal Article %J Group Processes & Intergroup Relations %D 2021 %T The role of race, religion, and partisanship in misinformation about COVID-19 %A James Druckman %A Katherine Ognyanova %A Matthew Baum %A Lazer, David %A Roy Perlis %A John Della Volpe %A Santillana, Mauricio %A H Chwe %A Alexi Quintana %A Matthew Simonson %B Group Processes & Intergroup Relations %V 24 %P 638–657 %G eng %N 4 %0 Journal Article %J JAMA Network Open %D 2021 %T Factors associated with self-reported symptoms of depression among adults with and without a previous COVID-19 diagnosis %A Roy Perlis %A Santillana, Mauricio %A Katherine Ognyanova %A Jon Green %A James Druckman %A Lazer, David %A Matthew Baum %B JAMA Network Open %V 4 %G eng %N 6 %0 Journal Article %J PLoS Neglected Tropical Diseases %D 2021 %T Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil %A Lauren A. Castro %A Nicholas Generous %A Wei Luo %A Ana Pastore y Piontti %A Kaitlyn Martinez %A Marcelo F.C. Gomes %A Dave Osthus %A Geoffrey Fairchild %A Amanda Ziemann %A Alessandro Vespignani %A Santillana, Mauricio %A Carrie A. Manore %A Sara Y. Del Valle %X Dengue virus is spread through mosquitoes in many tropical and subtropical parts of Objective: To create a machine learning model identifying potentially avoidable blood draws for
serum potassium among pediatric patients following cardiac surgery.
Design:Retrospective cohort study.
Setting: Tertiary-care center.
Patients: All patients admitted to the CICU at Boston Children’s Hospital between January 2010
and December 2018 with a length of stay ≥4 days and ≥2 recorded serum potassium
measurements.
Interventions None.
Measurements and Main Results
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 hyperparameters, we
created models predicting whether a patient’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, 95,674 serum potassium was measured on average of 1 (IQR 0-1) time per day. 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%, 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.
Conclusions
Machine-learning methods can be used to accurately predict avoidable blood tests 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.
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.
%B Cancer Discovery %V DOI: 10.1158/2159-8290.CD-20-0422 %G eng %0 Journal Article %J Nature %D 2020 %T Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China %A Shengjie Lai %A Nick W Ruktanonchai %A Liangcai Zhou %A Olivia Prosper %A Wei Luo %A Jessica R Floyd %A Amy Wesolowski %A Santillana, Mauricio %A Zhang, Chi %A Xiangjun Du %A Hongjie Yu %A Tatem, Andrew J %X 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<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. %B Nature %V https://doi.org/10.1038/s41586-020-2293-x %G eng %U https://doi.org/10.1038/s41586-020-2293-x %0 Journal Article %J SSRN %D 2020 %T 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 %A Justin Kaashoek %A Santillana, Mauricio %X 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. %B SSRN %G eng %0 Journal Article %J Scientific Reports %D 2020 %T The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales. %A Canelle Poirier %A Wei Luo %A Maimuna S. Majumder %A Dianbo Liu %A Mandl, Kenneth %A Todd Mooring %A Santillana, Mauricio %X 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. %B Scientific Reports %V 10 %P 17002 %G eng %0 Journal Article %J Journal of Medical Internet Research %D 2020 %T 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 %A Dianbo Liu %A Clemente, Leonardo %A Canelle Poirier %A Xiyu Ding %A Matteo Chinazzi %A Jessica T Davis %A Alessandro Vespignani %A Santillana, Mauricio %X 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'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. %B Journal of Medical Internet Research %V 22 %G eng %N 8 %0 Journal Article %J arXiv %D 2020 %T Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States %A Parker Liautaud %A Huybers, Peter %A Santillana, Mauricio %X 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<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. %B arXiv %G eng %0 Journal Article %J medRxiv %D 2020 %T The role of absolute humidity on transmission rates of the COVID-19 outbreak. %A Luo, W %A M S Majumder %A D Liu %A C Poirier %A Mandl, K. %A M. Lipsitch %A Santillana, Mauricio %X 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. %B medRxiv %G eng %0 Journal Article %J Lancet Digital Health %D 2020 %T Fitbit-informed influenza forecasts %A Cecile Viboud %A Santillana, Mauricio %B Lancet Digital Health %V 2 %G eng %N 2 %0 Journal Article %J PLoS Computational Biology %D 2020 %T Real-time Estimation of Disease Activity in EmergingOutbreaks using Internet Search Information %A Emily Aiken %A Sarah McGough %A Maia Majumder %A Gal Wachtel %A Andre T Nguyen %A Cecile Viboud %A Santillana, Mauricio %X 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. %B PLoS Computational Biology %G eng %0 Journal Article %J Geoscientific Model Development %D 2020 %T An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models %A Shen, Lu %A Daniel J. Jacob %A Santillana, Mauricio %A Xuan Wang %A Chen, Wei %X 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. %B Geoscientific Model Development %V 13 %P 2475–2486 %G eng %0 Journal Article %J Respiratory Care %D 2020 %T Adding Continuous Vital-Sign Information to Static Clinical Data Improves Prediction of Length of Stay Following Intubation. A Data Driven Machine Learning Approach %A D Castiñeira %A K Schlosser %A A Geva %A A Rahmani %A G Fiore %A B K Walsh %A C D Smallwood %A J H Arnold %A Santillana, M %X Background. Bedside monitors in intensive care units (ICUs) routinely measure and collect patients’ 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’ ICU hospitalization.
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’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’ 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.
%B Respiratory Care %V 65 %G eng %N 9 %0 Journal Article %J Eurosurveillance %D 2020 %T Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000-2016 %A Sarah McGough %A Derek R. MacFadden %A Mohammad W Hattab %A Kåre Mølbak %A Santillana, Mauricio %XBackground. 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°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.
%B Eurosurveillance %V 25 %P 1900414 %G eng %N 45 %0 Journal Article %J Gates Open Research %D 2019 %T Leveraging Google Search Data to Track Influenza Outbreaks in Africa %A Karla Mejia %A Cecile Viboud %A Santillana, Mauricio %X
Background: Traditionally, public health agencies track seasonal influenza activity by collecting information from clinics,hospitals, and laboratories. 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. 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
%B Gates Open Research %V 3 %P 1653 %G eng %0 Book Section %B Global Catastrophic Biological Risk %D 2019 %T Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic %A Marc Lipsitch %A Santillana, Mauricio %E Tom Inglesby %XCatastrophic 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.
%B Global Catastrophic Biological Risk %I Current Topics in Microbiology and Immunology. Springer, Berlin, Heidelberg. %G eng %U https://link.springer.com/chapter/10.1007/82_2019_172 %0 Journal Article %J PLoS Neglected Tropical Diseases %D 2019 %T Chikungunya virus outbreak in the Amazon region: replacement of the Asian genotype by an ECSA lineage %A F G Naveca %A I Claro %A M Giovanetti %A J G Jesus %A J Javier %A F C M Iani %A V A do Nascimento %A V C Souza %A P P Silveira %A J Lourenco %A Santillana, M %A M Kraemer %A N R Faria %B PLoS Neglected Tropical Diseases %V 13 %P e0007065 %G eng %N 3 %0 Journal Article %J JMIR Public Health Surveillance %D 2019 %T Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation %A Baltrusaitis, Kristin %A Alessandro Vespignani %A Roni Rosenfeld %A Gray, Josh %A Dorrie Raymond %A Santillana, Mauricio %XBackground: 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—CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org—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’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–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–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.
%B JMIR Public Health Surveillance %V 5 %P e13403 %G eng %N 4 %0 Journal Article %J Respiratory Care %D 2019 %T Noninvasive Ventilation Is Interrupted Frequently and Mostly Used at Night in the Pediatric Intensive Care Unit %A Katherine R. Schlosser %A Gaston A. Fiore %A Craig D. Smallwood %A John Griffin %A Geva, Alon %A Santillana, Mauricio %A Arnold, John H. %XBackground: 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.
%B Respiratory Care %V 64 %G eng %N 9 %0 Journal Article %J JMIR Public Health Surveillance %D 2019 %T Improved real-time influenza surveillance using Internet search data in eight Latin American countries %A Leonardo C Clemente %A Lu, Fred %A Santillana, Mauricio %XBackground: 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’ 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.
%B JMIR Public Health Surveillance %V 5 %P e12214 %G eng %N 2 %0 Journal Article %J Nature Communications %D 2019 %T Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet %A Lu, Fred %A Hattab, Mohammad %A Clemente, Leonardo %A Santillana, Mauricio %B Nature Communications %V 10 %G eng %N 147 %0 Journal Article %J Journal of the American Medical Informatics Association %D 2019 %T Internet search query data improves forecasts of daily emergency department volume %A S Tideman %A Santillana, M %A Bickel, J. %A Reis, B. %B Journal of the American Medical Informatics Association %V ocz154 %G eng %0 Journal Article %J PLOS Currents Outbreaks %D 2018 %T Estimation of Pneumonic Plague Transmission in Madagascar, August–November 2017 %A Majumder, Maia S. %A Cohn, Emily L %A Santillana, Mauricio %A J. S. Brownstein %B PLOS Currents Outbreaks %V 1 %G eng %0 Journal Article %J BMC infectious diseases %D 2018 %T 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 %A K. Baltrusaitis %A J. Brownstein %A S. Scarpino %A E. Bakota %A A. Crawley %A J. Conidi %A J. Gunn %A J. Gray %A Zink, A. %A Santillana, M. %B BMC infectious diseases %V 18 %G eng %N 403 %0 Journal Article %J Journal of Medical Internet Research %D 2018 %T Accurate influenza monitoring and forecasting in the Boston metropolis using novel Internet data streams. %A F. Lu %A S Hou %A K Baltrusaitis %A M. Shah %A J. Leskovec %A R. Sosic %A J. Hawkins %A J. S. Brownstein %A G. Conidi %A J. Gunn %A J. Gray %A Zink, A. %A Santillana, M. %B Journal of Medical Internet Research %V 4 %P e4 %G eng %N 1 %0 Journal Article %J Nature Climate Change %D 2018 %T Antibiotic Resistance Increases with Local Temperature. %A D. R. MacFadden %A S.F. McGough %A D. Fisman %A Santillana, M. %A J. S. Brownstein %B Nature Climate Change %V 8 %P 510–514 %G eng %0 Journal Article %J Infectious Disease Modelling %D 2018 %T Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model," Farr's Law" and SIR Compartmental Difference Equation Models %A Santillana, Mauricio %A Tuite, Ashleigh %A Nasserie, Tahmina %A Fine, Paul %A Champredon, David %A Chindelevitch, Leonid %A Dushoff, Jonathan %A Fisman, David %B Infectious Disease Modelling %V 3 %P 1-12 %G eng %0 Journal Article %J Journal of Medical Internet Research Public Health Surveillance %D 2017 %T Combining Participatory Influenza Surveillance with Modeling and Forecasting %A A. Marathe %A J. S. Brownstein %A S. Chu %A M. V. Marathe %A A. T. Nguyen %A D. Paolotti %A N. Perra %A D. Perrotta %A Santillana, M. %A S. Swarup %A M. Tizzoni %A A. Vespignani %A A. K. S. Vullikanti %A M. L. Wilson %A Q. Zhang %B Journal of Medical Internet Research Public Health Surveillance %V 3 %P e83 %G eng %N 4 %0 Journal Article %J Vaccine %D 2017 %T County-level assessment of United States kindergarten vaccination rates for measles mumps rubella (MMR) for the 2014--2015 school year %A S. A. Kluberg %A D. P. McGinnis %A Y. Hswen %A M. S. Majumder %A Santillana, M. %A J. S. Brownstein %B Vaccine %V 35 %P 6444-6450 %G eng %0 Journal Article %J JMIR Public Health Surveillance %D 2017 %T Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveilla %A Baltrusaitis, Kristin %A Santillana, Mauricio %A Adam Crawley %A Chunara, R. %A Mark Smolinski %A John Brownstein %B JMIR Public Health Surveillance %V 3 %P e18 %G eng %N 3 %0 Journal Article %J PLoS Computational Biology %D 2017 %T Advances in the use of Google searches to track dengue in Mexico, Brazil, Thailand, Singapore and Taiwan %A Yang, S. %A S. C. Kou %A F. Lu %A J. S. Brownstein %A N. Brooke %A Santillana, M %B PLoS Computational Biology %V 13 %P e1005607 %8 Jul, 2017 %G eng %N 7 %0 Journal Article %J BMC infectious diseases %D 2017 %T Using electronic health records and Internet search information for enhanced influenza forecast %A Yang, S. %A Santillana, M. %A J. S. Brownstein %A J. Gray %A S. Richardson %A S. C Kou %B BMC infectious diseases %V 17 %P 332 %8 May, 2017 %G eng %N 1 %0 Journal Article %J PLoS Neglected Tropical Diseases %D 2017 %T Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. %A S.F. McGough %A J. S. Brownstein %A J. Hawkins %A Santillana, M. %B PLoS Neglected Tropical Diseases %V 11 %P e0005295 %G eng %N 1 %0 Journal Article %J Clinical Infectious Diseases %D 2017 %T Perspectives on the future of Internet search engines and biosurveillance systems. %A Santillana, M. %B Clinical Infectious Diseases %V 64 %P 42-43 %8 Sep 2016 %G eng %N 1 %0 Journal Article %J Scientific Reports %D 2016 %T Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico %A M.A. Johansson %A N.G. Reich %A A. Hota %A J. S. Brownstein %A Santillana, M. %B Scientific Reports %V 6 %G eng %N 33707 %0 Journal Article %J Scientific reports %D 2016 %T Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance %A Santillana, Mauricio %A Nguyen, AT %A Louie, Tamara %A Zink, Anna %A Gray, Josh %A Sung, Iyue %A Brownstein, John S %B Scientific reports %I Nature Publishing Group %V 6 %G eng %0 Journal Article %J JMIR public health and surveillance %D 2016 %T Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak %A Majumder, Maimuna S %A Santillana, Mauricio %A Mekaru, Sumiko R %A McGinnis, Denise P %A Khan, Kamran %A Brownstein, John S %B JMIR public health and surveillance %I JMIR Publications Inc., Toronto, Canada %V 2 %P e30 %G eng %N 1 %0 Journal Article %J Journal of Computational Physics %D 2016 %T Estimating numerical errors due to operator splitting in global atmospheric chemistry models: Transport and chemistry %A Santillana, Mauricio %A Zhang, Lin %A Yantosca, Robert %B Journal of Computational Physics %I Academic Press %V 305 %P 372–386 %G eng %0 Journal Article %J PLoS Currents Outbreaks %D 2015 %T 2014 Ebola outbreak: Media events track changes in observed reproductive number %A Majumder, Maimuna S %A Kluberg, Sheryl %A Santillana, Mauricio %A Mekaru, Sumiko %A Brownstein, John S %B PLoS Currents Outbreaks %I Public Library of Science %G eng %0 Journal Article %J American journal of public health %D 2015 %T Flu Near You: crowdsourced symptom reporting spanning 2 influenza seasons %A Smolinski, Mark S %A Crawley, Adam W %A Baltrusaitis, Kristin %A Chunara, Rumi %A Olsen, Jennifer M %A Wójcik, Oktawia %A Santillana, Mauricio %A Nguyen, Andre %A Brownstein, John S %B American journal of public health %I American Public Health Association %V 105 %P 2124–2130 %G eng %N 10 %0 Journal Article %J Proceedings of the National Academy of Sciences %D 2015 %T Accurate estimation of influenza epidemics using Google search data via ARGO %A Yang, Shihao %A Santillana, Mauricio %A Kou, SC %B Proceedings of the National Academy of Sciences %I National Acad Sciences %V 112 %P 14473–14478 %G eng %N 47 %0 Journal Article %J PLoS Comput Biol %D 2015 %T Combining search, social media, and traditional data sources to improve influenza surveillance %A Santillana, Mauricio %A Nguyen, André T %A Dredze, Mark %A Paul, Michael J %A Nsoesie, Elaine O %A Brownstein, John S %B PLoS Comput Biol %I Public Library of Science %V 11 %P e1004513 %G eng %N 10 %0 Journal Article %J Journal of medical Internet research %D 2014 %T A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives %A Nagar, Ruchit %A Yuan, Qingyu %A Freifeld, Clark C %A Santillana, Mauricio %A Nojima, Aaron %A Chunara, Rumi %A Brownstein, John S %B Journal of medical Internet research %I JMIR Publications Inc., Toronto, Canada %V 16 %P e236 %G eng %N 10 %0 Journal Article %J PLoS Negl Trop Dis %D 2014 %T Evaluation of Internet-based dengue query data: Google Dengue Trends %A Gluskin, Rebecca Tave %A Johansson, Michael A %A Santillana, Mauricio %A Brownstein, John S %B PLoS Negl Trop Dis %I Public Library of Science %V 8 %P e2713 %G eng %N 2 %0 Journal Article %J Clinical Infectious Diseases %D 2014 %T Using clinicians’ search query data to monitor influenza epidemics %A Santillana, Mauricio %A Nsoesie, Elaine O %A Mekaru, Sumiko R %A Scales, David %A Brownstein, John S %B Clinical Infectious Diseases %I Oxford University Press %V 59 %P 1446–1450 %G eng %N 10 %0 Journal Article %J American journal of preventive medicine %D 2014 %T What can digital disease detection learn from (an external revision to) Google Flu Trends? %A Santillana, Mauricio %A Zhang, D Wendong %A Althouse, Benjamin M %A Ayers, John W %B American journal of preventive medicine %I Elsevier %V 47 %P 341–347 %G eng %N 3 %0 Journal Article %J arXiv preprint arXiv:1311.6315 %D 2013 %T Quantifying the loss of information in source attribution problems using the adjoint method in global models of atmospheric chemical transport %A Santillana, Mauricio %B arXiv preprint arXiv:1311.6315 %G eng %0 Journal Article %J Journal of Computational and Applied Mathematics %D 2013 %T Gradient-based estimation of Manning’s friction coefficient from noisy data %A Calo, Victor M %A Collier, Nathan %A Gehre, Matthias %A Jin, Bangti %A Radwan, Hany %A Santillana, Mauricio %B Journal of Computational and Applied Mathematics %I North-Holland %V 238 %P 1–13 %G eng %0 Journal Article %J Journal of the Serbian Society for Computational Mechanics/Vol %D 2012 %T Convergence rates for diffusive shallow water equations (DSW) using higher order polynomials %A Radwan, HG %A Vignal, P %A Collier, N %A Dalcin, L %A Santillana, M %A Calo, VM %B Journal of the Serbian Society for Computational Mechanics/Vol %V 6 %P 160–168 %G eng %N 1 %0 Journal Article %J Atmospheric Environment %D 2010 %T An adaptive reduction algorithm for efficient chemical calculations in global atmospheric chemistry models %A Santillana, Mauricio %A Le Sager, Philippe %A Daniel J. Jacob %A Brenner, Michael P %B Atmospheric Environment %I Elsevier %V 44 %P 4426–4431 %G eng %N 35 %0 Journal Article %J International Journal of Remote Sensing %D 2010 %T Estimating small-area population growth using geographic-knowledge-guided cellular automata %A Zhan, F Benjamin %A Tapia Silva, Felipe Omar %A Santillana, Mauricio %B International Journal of Remote Sensing %I Taylor & Francis %V 31 %P 5689–5707 %G eng %N 21 %0 Journal Article %J Computer Methods in Applied Mechanics and Engineering %D 2010 %T A local discontinuous Galerkin method for a doubly nonlinear diffusion equation arising in shallow water modeling %A Santillana, Mauricio %A Dawson, Clint %B Computer Methods in Applied Mechanics and Engineering %I North-Holland %V 199 %P 1424–1436 %G eng %N 23 %0 Journal Article %J Computational Geosciences %D 2010 %T A numerical approach to study the properties of solutions of the diffusive wave approximation of the shallow water equations %A Santillana, Mauricio %A Dawson, Clint %B Computational Geosciences %I Springer %V 14 %P 31–53 %G eng %N 1 %0 Book %D 2008 %T Analysis and numerical simulation of the diffusive wave approximation of the shallow water equations %A Santillana, Mauricio %I ProQuest %G eng %0 Journal Article %J European Journal of Applied Mathematics %D 2008 %T On the diffusive wave approximation of the shallow water equations %A Alonso, R %A Santillana, Mauricio %A Dawson, Clint %B European Journal of Applied Mathematics %I Cambridge University Press %V 19 %P 575–606 %G eng %N 05