I am an applied mathematician and physicist with expertise in mathematical modeling and scientific computing. I specialize in the analysis of big data sets in multiple contexts to understand and predict the behavior of complex systems. I also have expertise in the design and analysis of numerical methods to solve partial differential equations (PDEs) for a diverse array of applications.
Implementing and validating methodologies to solve challenging problems, in real-life computational applications, has been central to my research, and has exposed me to the use of high–performance computing environments.
I am interested in designing and implementing novel ways to track epidemics in real-time using data from Google, Twitter, Facebook, UpToDate (digital disease detection). I am conducting research to understand the impact of climate change on disease burden in Africa and Mexico. I have also conducted research in atmospheric pollution transport modeling, climate modeling, modeling of floods due to hurricanes,and population dynamics modeling. The following paragraphs summarize my research contributions.
1. Digital Epidemiology: Design and implementation of Digital Disease Detection tools as decision-support tools to help public health officials make timely decisions and prevent epidemic outbreaks.
We are working towards the development of the next generation of digital epidemiolgy tools aimed at monitoring disease outbreaks in real-time. We are constantly developing methodologies that leverage information from multiple data-sources including Google search query patterns, Twitter microblogs, cloud-based electronic health records, weather, and human mobility, to produce real-time and short-term forecast estimates of endemic disease outbreaks of dengue, flu and emerging disease outbreaks such as the COVID-19 Pandemic, the 2014 West African Ebola outbreak, the 2014 Latin American Zika outbreak, and the ongoing 2019-nCoV outbreak originated in Wuhan, China. Our real-time estimates are typically available 1-4 weeks earlier than the official CDC-reported flu activity in the US. The Machine Intelligence Lab at Boston Children's Hospital / Harvard Medical School is our computational laboratory.
Figure. We are constantly striving to improve our real-time disease estimates with the goal of assisting public health officials make informed decisions to mitigate the effects of disease outbreaks. Our platform will soon display disease estimates for Dengue, Flu, and a diverse arrray of emrging outbreaks throughout the world.
Related publications:
An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time
Kogan NE, Clemente L, Liautaud P, Kaashoek J, Link NB, Nguyen AT, Lu FS, Huybers P, Resch B, Havas C, et al. Science Advances. 2021;7 (10).
Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States
Lucas M Stolerman, Leonardo Clemente, Canelle Poirier, Kris V Parag, Atreyee Majumder, Serge Masyn, Bernd Resch, Mauricio Santillana. Science Advances. 2023; Vol 9: Issue 3.
Using general messages to persuade on a politicized scientific issue
Jon Green, James N Druckman, Matthew A Baum, David Lazer, Katherine Ognyanova, Matthew D Simonson, Jennifer Lin, Mauricio Santillana, Roy H Perlis. British Journal of Political Science. 2023; 53(2)698-706.
Gastroenteritis Forecasting Assessing the Use of Web and Electronic Health Record Data With a Linear and a Nonlinear Approach: Comparison Study
Canelle Poirier, Guillaume Bouzillé, Valérie Bertaud, Marc Cuggia, Mauricio Santillana, Audrey Lavenu
JMIR Public Health and Surveillance. 2023; 9;e34982.
Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches
Lu F, Hattab M, Clemente L, Santillana M. Nature Communications. 2019;10 (147).
Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions
Aiken EL, Nguyen AT, Viboud C, Santillana M. Science Advances. 2021;7 (25).
Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China.
Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, Wesolowski A, Santillana M, Zhang C, Du X, et al. Nature. 2020;https://doi.org/10.1038/s41586-020-2293-x.
Combining search, social media, and traditional data sources to improve influenza surveillance
Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. PLoS Comput Biol. 2015;11 (10):e1004513.
Accurate estimation of influenza epidemics using Google search data via ARGO
Yang S, Santillana M, Kou SC. Proceedings of the National Academy of Sciences. 2015;112 (47): 14473–14478.
What can digital disease detection learn from (an external revision to) Google Flu Trends?
Santillana M, Zhang WD, Althouse BM, Ayers JW. American journal of preventive medicine. 2014;47 (3) :341–347.
Accurate influenza monitoring and forecasting in the Boston metropolis using novel Internet data streams
Lu F, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, Hawkins J, Brownstein JS, Conidi G, Gunn J, et al. Journal of Medical Internet Research. 2018;4 (1) :e4.
SARS-CoV-2 RNA concentrations in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases
Fuqing Wu, Amy Xiao, Jianbo Zhang, Katya Moniz, Noriko Endo, Federica Armas, Richard Bonneau, Megan A Brown, Mary Bushman, Peter R Chai, Claire Duvallet, Timothy B Erickson, Katelyn Foppe, Newsha Ghaeli, Xiaoqiong Gu, William P Hanage, Katherine H Huang, Wei Lin Lee, Mariana Matus, Kyle A McElroy, Jonathan Nagler, Steven F Rhode, Mauricio Santillana, Joshua A Tucker, Stefan Wuertz, Shijie Zhao, Janelle Thompson, Eric J Alm. Science of The Total Environment 805, 1501211452022
Advances in the use of Google searches to track dengue in Mexico, Brazil, Thailand, Singapore and Taiwan
Yang S, Kou SC, Lu F, Brownstein JS, Brooke N, Santillana M. PLoS Computational Biology. 2017;13 (7):e1005607.
Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data.
McGough SF, Brownstein JS, Hawkins J, Santillana M. PLoS Neglected Tropical Diseases. 2017;11 (1):e0005295.
Real-time Estimation of Disease Activity in Emerging Outbreaks using Internet Search Information
Aiken E, McGough S, Majumder M, Wachtel G, Nguyen AT, Viboud C, Santillana M. PLoS. Computational Biology. 2020.
Using electronic health records and Internet search information for enhanced influenza forecast
Yang S, Santillana M, Brownstein JS, Gray J, Richardson S, Kou SC. BMC infectious diseases. 2017;17(1) :332.
Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance
Santillana M, Nguyen AT, Louie T, Zink A, Gray J, Sung I, Brownstein JS. Scientific reports. 2016;6.
Aggregated mobility data could help fight COVID-19
Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, Grad YH, Grenfell B, Halloran ME, Kraemer MUG, et al. Science. 2020;368 (6487) :145-146.
Incorporating human mobility data improves forecasts of Dengue fever in Thailand
Kiang MV, Santillana M, Chen JT, Onnela J-P, Krieger N, Engø-Monsen K, Ekapirat N, Areechokchai D, Maude R, Buckee CO.Scientific Reports. 2021;11 (923).
Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil
Castro LA, Generous N, Luo W, y Piontti AP, Martinez K, Gomes MFC, Osthus D, Fairchild G, Ziemann A, Vespignani A, et al. PLoS Neglected Tropical Diseases. 2021.
A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
McGough S, Kutz NJ, Clemente LC, Santillana M. Journal of the Royal Society Interface. 2021;18 (179)
Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach
Canelle Poirier, Yulin Hswen, Guillaume Bouzillé, Marc Cuggia, Audrey Lavenu, John S Brownstein, Thomas Brewer, Mauricio Santillana. PloS one 16 (5), e02508902021
High-resolution Spatio-temporal Model for County-level COVID-19 Activity in the US
S Zhu, A Bukharin, L Xie, M Santillana, S Yang, Y Xie. ACM Transactions on Management Information Systems (TMIS) 12 (4), 1-20162021
A nowcasting framework for correcting for reporting delays in malaria surveillance
Tigist F Menkir, Horace Cox, Canelle Poirier, Melanie Saul, Sharon Jones-Weekes, Collette Clementson, Pablo M. de Salazar, Mauricio Santillana, Caroline O Buckee. PLoS Computational Biology 17 (11), e10095702021
Predicting dengue incidence leveraging internet-based data sources. A case study in 20 cities in Brazil
G Koplewitz, F Lu, L Clemente, C Buckee, M Santillana. PLoS neglected tropical diseases 16 (1), e00100712022
Fitbit-informed influenza forecasts
Viboud C, Santillana M. Lancet Digital Health. 2020;2 (2).
Leveraging Google Search Data to Track Influenza Outbreaks in Africa
Mejia K, Viboud C, Santillana M. Gates Open Research. 2019;3 :1653.
Chikungunya virus outbreak in the Amazon region: replacement of the Asian genotype by an ECSA lineage
Naveca FG, Claro I, Giovanetti M, Jesus JG, Javier J, Iani FCM, do Nascimento VA, Souza VC, Silveira PP, Lourenco J, et al. PLoS Neglected Tropical Diseases. 2019;13 (3) :e0007065.
Improved real-time influenza surveillance using Internet search data in eight Latin American countries
Clemente LC, Lu F, Santillana M. JMIR Public Health Surveillance. 2019;5 (2) :e12214.
Combining Participatory Influenza Surveillance with Modeling and Forecasting
Marathe A, Brownstein JS, Chu S, Marathe MV, Nguyen AT, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, et al. Journal of Medical Internet Research Public Health Surveillance. 2017;3 (4) :e83.
2. Characterizing Disease Outbreaks and Pandemic Preparedness
Catastrophic epidemics, when they occur, typically 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. Our team works in the design of mathematical strategies to provide decision makers and public health officials with high-quality situation reporting, by providing real-time estimates of key epidemiological quantities (cumulative incidence, effective reproductive number, the likelihood that an outbreak will occur in the near future in a given location) to help them make informed decisions, leveraging traditional and novel data sources.
Figure Key decisions on pandemic response and the evidence base on which they ideally rest; this evidence base is built up from surveillance inputs using interpretive tools such as transmission-dynamic models and “pyramid”severity models. Image adapted from Lipsitch et al. (2011) by Lucia Ricci
Related publications:
Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic
Lipsitch M, Santillana M. In: Inglesby T Global Catastrophic Biological Risk. Current Topics in Microbiology and Immunology. Springer, Berlin, Heidelberg. ; 2019.
Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multi-Center Study During the COVID-19 Outbreak
Dai M-Y, Liu D, Liu M, Zhou F-X, .., Mucci LA, Santillana M, Cai H-B. Cancer Discovery. 2020;DOI: 10.1158/2159-8290.CD-20-0422.
Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile
Mena G, Martinez PP, Mahmud AS, Marquet PA, Buckee CO, Santillana M. Science. 2021 :eabg5298.
Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China.
Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, Wesolowski A, Santillana M, Zhang C, Du X, et al. Nature . 2020;https://doi.org/10.1038/s41586-020-2293-x.
Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches
Lu FS, Nguyen AT, Link N, Molina M, Davis JT, Chinazzi M, Xiong X, Vespignani A, Lipsitch M, Santillana M. PLoS Computational Biology . 2021.
COVID-19: US Federal accountability for entry, spread, and inequities – lessons for the future
Hanage WP, Testa C, Chen JT, David L, Pechter E, Seminario P, Santillana M, Krieger N. European Journal of Epidemiology. 2020.
The Evolving Roles of US Political Partisanship and Social Vulnerability in the COVID-19 Pandemic from February 2020 - February 2021
J Kaashoek, C Testa, J Chen, L Stolerman, N Krieger, WP Hanage, and Mauricio Santillana. Available at SSRN 3933453
Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data
Pablo M De Salazar, Fred Lu, James A Hay, Diana Gómez-Barroso, Pablo Fernández-Navarro, Elena V Martínez, Jenaro Astray-Mochales, Rocío Amillategui, Ana García-Fulgueiras, Maria D Chirlaque, Alonso Sánchez-Migallón, Amparo Larrauri, María J Sierra, Marc Lipsitch, Fernando Simón, Mauricio Santillana, Miguel A Hernán. PLoS computational biology 18 (3), e100996442022
Leveraging Serosurveillance and Postmortem Surveillance to Quantify the Impact of COVID-19 in Africa
Nicole Kogan, Shae Gantt, David Swerdlow, Cecile Viboud, Muhammed Semakula, Marc Lipsitch, Mauricio Santillana. medRxiv2022
High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in Long-Term Care Facilities
de Salazar P, Link N, Lamarca K, Santillana M. Nature Communications Medicine. 2021;1 (16).
Characterizing features of outbreak duration for novel SARS-CoV-2 variants of concern
AD Washburne, N Hupert, N Kogan, W Hanage, M Santillana. medRxiv2022
A nowcasting framework for correcting for reporting delays in malaria surveillance
Tigist F Menkir, Horace Cox, Canelle Poirier, Melanie Saul, Sharon Jones-Weekes, Collette Clementson, Pablo M. de Salazar, Mauricio Santillana, Caroline O Buckee. PLoS Computational Biology 17 (11), e10095702021
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
Kaashoek J, Santillana M. SSRN. 2020.
Communicating Benefits from Vaccines Beyond Preventing Infectious Diseases
Emma-Pascale Chevalier-Cottin, Hayley Ashbaugh, Nicholas Brooke, Gaetan Gavazzi, Mauricio Santillana, Nansa Burlet, Myint Tin Tin Htar. Infectious Diseases and Therapy. 2020;9:467–480.
Estimation of Pneumonic Plague Transmission in Madagascar, August–November 2017
Majumder MS, Cohn EL, Santillana M, Brownstein JS. PLOS Currents Outbreaks. 2018;1.
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
Baltrusaitis K, Brownstein J, Scarpino S, Bakota E, Crawley A, Conidi J, Gunn J, Gray J, Zink A, Santillana M. BMC infectious diseases. 2018;18 (403).
Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model," Farr's Law" and SIR Compartmental Difference Equation Models
Santillana M, Tuite A, Nasserie T, Fine P, Champredon D, Chindelevitch L, Dushoff J, Fisman D. Infectious Disease Modelling. 2018;3 :1-12.
County-level assessment of United States kindergarten vaccination rates for measles mumps rubella (MMR) for the 2014--2015 school year
Kluberg SA, McGinnis DP, Hswen Y, Majumder MS, Santillana M, Brownstein JS. Vaccine. 2017;35 :6444-6450.
Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak
Majumder MS, Santillana M, Mekaru SR, McGinnis DP, Khan K, Brownstein JS. JMIR public health and surveillance. 2016;2 (1) :e30.
3. Monitoring Changes in Human Behaviors during the COVID-19 Pandemic
The 50-state COVID-19 project was launched in March 2020 by a multi-university group of researchers with expertise in computational social science, network science, public opinion polling, epidemiology, public health, communication, and political science. We aim to help practitioners and governments to make informed decisions and allocate resources more effectively. Our research seeks to identify links between social behaviors and virus transmission, as well as and the impact of messaging and regulation on individual and community outcomes during this crisis. We are sharing our data and insights directly with collaborators and decision-makers, as well as making our findings public online.
Our team is an active collaborator of this initiative and Prof. Santillana is one of the Principal Investigators in the COVID-states project.
Related publications:
A 50-state survey study of thoughts of suicide and social isolation among older adults in the United States Nili Solomonov, Jon Green, Alexi Quintana, Jennifer Lin, Katherine Ognyanova, Mauricio Santillana, James N Druckman, Matthew A Baum, David Lazer, Faith M Gunning, Roy H Perlis. Journal of affective disorders. 2023; 334:43-49.
Estimating the impact of the COVID-19 pandemic on dengue in Brazil
Kirstin Oliveira Roster, Tiago Martinelli, Colm Connaughton, Mauricio Santillana, Francisco Rodrigues. Research Square. 2023; rs. 3. rs-2548491.
Association of post–COVID-19 condition symptoms and employment status
Roy H Perlis, Kristin Lunz Trujillo, Alauna Safarpour, Mauricio Santillana, Katherine Ognyanova, James Druckman, David Lazer. JAMA network open. 2023; 6(2):e2256152-e2256152.
Correlates of symptomatic remission among individuals with post-COVID-19 condition
Roy H Perlis, Mauricio Santillana, Katherine Ognyanova, David Lazer. medRxiv. 2023.01. 31.23285246
Prevalence of firearm ownership among individuals with major depressive symptoms
Roy H Perlis, Matthew D Simonson, Jon Green, Jennifer Lin, Alauna Safarpour, Kristin Lunz Trujillo, Alexi Quintana, Hanyu Chwe, John Della Volpe, Katherine Ognyanova, Mauricio Santillana, James Druckman, David Lazer, Matthew A Baum. JAMA network open 5 (3), e223245-e22324512022
Association of major depressive symptoms with endorsement of COVID-19 vaccine misinformation among US adults
Roy H Perlis, Katherine Ognyanova, Mauricio Santillana, Jennifer Lin, James Druckman, David Lazer, Jon Green, Matthew Simonson, Matthew A Baum, John Della Volpe. JAMA network open 5 (1), e2145697-e214569742022
Using general messages to persuade on a politicized scientific issue
Jon Green, James N Druckman, Matthew A Baum, David Lazer, Katherine Ognyanova, Matthew Simonson, Jennifer Lin, Mauricio Santillana, Roy H Perlis. British Journal of Political Science
Association between social media use and self-reported symptoms of depression in US adults
Roy H Perlis, Jon Green, Matthew Simonson, Katherine Ognyanova, Mauricio Santillana, Jennifer Lin, Alexi Quintana, Hanyu Chwe, James Druckman, David Lazer, Matthew A Baum, John Della VolpeJAMA network open 4 (11), e2136113-e213611312021
Gender‐specificity of resilience in major depressive disorder
Roy H Perlis, Katherine Ognyanova, Alexi Quintana, Jon Green, Mauricio Santillana, Jennifer Lin, James Druckman, David Lazer, Matthew D Simonson, Matthew A Baum, Hanyu Chwe. Depression and anxiety 38 (10), 1026-103322021
The role of race, religion, and partisanship in misinformation about COVID-19
Druckman J, Ognyanova K, Baum M, Lazer D, Perlis R, Volpe JD, Santillana M, Chwe H, Quintana A, Simonson M. Group Processes & Intergroup Relations. 2021;24 (4) :638–657.
Perlis R, Santillana M, Ognyanova K, Green J, Druckman J, Lazer D, Baum M. JAMA Network Open. 2021;4 (6).
Comparison of post-COVID depression and major depressive disorder
Roy H Perlis, Mauricio Santillana, Katherine Ognyanova, Jon Green, James Druckman, David Lazer, Matthew A Baum. MedRxiv62021
Association of Acute Symptoms of COVID-19 and Symptoms of Depression in Adults
Perlis RH, Ognyanova K, Santillana M, Baum MA, Lazer D, Druckman J, Volpe JD. JAMA Network Open. 2021;4 (3) :e213223.
Persistence of symptoms up to 10 months following acute COVID-19 illness
Roy H Perlis, Jon Green, Mauricio Santillana, David Lazer, Katherine Ognyanova, Matthew Simonson, Matthew A Baum, Alexi Quintana, Hanyu Chwe, James Druckman, John Della Volpe, Jennifer Lin. MedRxiv
4. Climate and Health: Understanding the role of environmental factors on the timing of outbreaks of infectious diseases (Dengue, Antibiotic Resistance)
The availability of on-line and real-time sources of big data sets gives us the opportunity to monitor how climate variables (among many other factors) influence the spread of Dengue, Antibiotic Resistance, and other outbreaks around the world
Related publications:
Antibiotic Resistance Increases with Local Temperature
MacFadden DR, McGough SF, Fisman D, Santillana M, Brownstein JS. Nature Climate Change. 2018;8:510–514.
Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000-2016
McGough S, MacFadden DR, Hattab MW, Mølbak K, Santillana M. Eurosurveillance. 2020;25 (45):1900414.
The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales
Poirier C, Luo W, Majumder MS, Liu D, Mandl K, Mooring T, Santillana M. Scientific Reports. 2020;10:17002.
Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil
Castro LA, Generous N, Luo W, y Piontti AP, Martinez K, Gomes MFC, Osthus D, Fairchild G, Ziemann A, Vespignani A, et al. PLoS Neglected Tropical Diseases. 2021.
A dynamic, ensemble learning approach to forecast dengue fever epidemic years in Brazil using weather and population susceptibility cycles
McGough S, Kutz NJ, Clemente LC, Santillana M. Journal of the Royal Society Interface. 2021;18 (179)
Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico
Johansson MA, Reich NG, Hota A, Brownstein JS, Santillana M. Scientific Reports. 2016;6 (33707).
5. Improvement of patient care and hospital resource allocation
Figure: Stronger and more accurate prediction of how long a patient will stay in the Intensive Care Unit are obtained if you combine vital sign information with static clinical information.
Adding Continuous Vital-Sign Information to Static Clinical Data Improves Prediction of Length of Stay Following Intubation. A Data Driven Machine Learning Approach
Castiñeira D, Schlosser K, Geva A, Rahmani A, Fiore G, Walsh BK, Smallwood CD, Arnold JH, Santillana M. Respiratory Care. 2020;65 (9).
Avoidable serum potassium testing in the cardiac intensive care unit: development and testing of a machine learning model.
Patel B, Sperotto F, Molina M, Kimura S, Delgado M, Santillana M, Kheir JN. Pediatric Critical Care. Medicine. 2020;22 (4).
Machine learning approaches to predicting no-shows in pediatric medical appointment
Dianbo Liu, Won-Yong Shin, Eli Sprecher, Kathleen Conroy, Omar Santiago, Gal Wachtel, Mauricio Santillana. NPJ digital medicine 5 (1), 1-1112022
Noninvasive Ventilation Is Interrupted Frequently and Mostly Used at Night in the Pediatric Intensive Care Unit
Schlosser KR, Fiore GA, Smallwood CD, Griffin J, Geva A, Santillana M, Arnold JH. Respiratory Care. 2019;64 (9).
Internet search query data improves forecasts of daily emergency department volume
Tideman S, Santillana M, Bickel J, Reis B. Journal of the American Medical Informatics Association. 2019;ocz154.
6. Global atmospheric chemistry
Understanding the global-scale dynamics of the chemical composition of our atmosphere is essential for addressing a wide range of environmental issues from air quality to climate change. Understanding this phenomenon enables us to evaluate and devise appropriate environmental policies, such as the Kyoto Protocol on global greenhouse gases emissions. Numerical modeling of global atmospheric chemical dynamics presents an enormous challenge associated with simulating hundreds of chemical species with time scales varying from milliseconds to years.
In my research, I have worked on the implementation of computationally efficient algorithms for calculating the time evolution of the concentration of chemical species in global 3-D models of atmospheric chemistry. I have also investigated the efficacy of adjoint based inverse modeling techniques for source attribution problems.
Assessing model errors and uncertainties: I am very interested in understanding the large dependence of the results of Global Atmospheric Chemistry Models on spatial resolution, time stepping, and spatial averaging of meteorological fields, and the impact of these on the contaminant plume propagation.
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
L Shen, DJ Jacob, M Santillana, K Bates, J Zhuang, W Chen. Geoscientific Model Development 15 (4), 1677-16872022
An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models
Shen L, Jacob DJ, Santillana M, Wang X, Chen W. Geoscientific Model Development . 2020;13 :2475–2486.
Estimating numerical errors due to operator splitting in global atmospheric chemistry models: Transport and chemistry
Santillana M, Zhang L, Yantosca R. Journal of Computational Physics. 2016;305 :372–386.
An adaptive reduction algorithm for efficient chemical calculations in global atmospheric chemistry models
Santillana M, Le Sager P, Jacob DJ, Brenner MP. Atmospheric Environment. 2010;44 (35) :4426–4431.
Quantifying the loss of information in source attribution problems using the adjoint method in global models of atmospheric chemical transport
Santillana M. arXiv preprint arXiv:1311.6315. 2013.
7. Computational Fluid Dynamics: Shallow water modeling
As a consequence of our changing climate, large efforts have been made to understand the social risks of storm surges (hypothesized to increase in frequency in warmer climate scenarios) and sea level rise in coastal areas. Of particular interest is the role that wetlands and coastal marshes play in storm surges and flooding events.
For example, coastal marshes and swamps act as a buffer zone between the Gulf of Mexico and inhabited inland areas in Louisiana, where an estimated 60-75 % of residents live within 50 miles of the coast (1993) and where, between 1899 and 1995, over a dozen major hurricanes (class 3-5) have hit (with the two most recent hits being the category 5 hurricanes Katrina and Rita in 2005). Understanding the role of these rich biological ecosystems in our changing climate requires the development of appropriate mathematical models.
In my research, I have studied analytically and numerically an effective equation often referred to in the literature as the diffusive wave approximation of the shallow water system of equations (DSW), used to simulate overland flow in wetlands and open channels. This equation is obtained by approximating the depth averaged continuity equations by empirical laws such as Manning’s or Chezy’s formulas and then combining the resulting expression with the free surface boundary condition.
I have studied the properties of approximate (weak) solutions to the DSW using the continuous and discontinuous (LDG) Galerkin method, developing error estimates and implementing a 2-D code aimed at simulating water flow on experimental settings as well as real environments. I have also investigated inverse modeling approaches to estimate friction coefficients using the DSW as a physical model.
Related publications:
A numerical approach to study the properties of solutions of the diffusive wave approximation of the shallow water equations
Santillana M, Dawson C. Computational Geosciences. 2010;14 (1) :31–53.
On the diffusive wave approximation of the shallow water equations
Alonso R, Santillana M, Dawson C. European Journal of Applied Mathematics. 2008;19 (05) :575–606.
A local discontinuous Galerkin method for a doubly nonlinear diffusion equation arising in shallow water modeling
Santillana M, Dawson C. Computer Methods in Applied Mechanics and Engineering. 2010;199 (23):1424–1436.
Gradient-based estimation of Manning’s friction coefficient from noisy data
Calo VM, Collier N, Gehre M, Jin B, Radwan H, Santillana M. Journal of Computational and Applied. Mathematics. 2013;238 :1–13.
Convergence rates for diffusive shallow water equations (DSW) using higher order polynomials
Radwan HG, Vignal P, Collier N, Dalcin L, Santillana M, Calo VM. Journal of the Serbian Society for Computational Mechanics/Vol. 2012;6 (1) :160–168.
Analysis and numerical simulation of the diffusive wave approximation of the shallow water equations
Santillana M. ProQuest; 2008.