ABOUT THE RESEARCHER

OVERVIEW

Using social media, Internet searches, and electronic health records to predict incidence of flu and dengue in multiple locations worldwide. Using electronic health records to predict outcomes in pediatric intensive care units.

BACKGROUND

Mauricio Santillana is an Assistant Professor at Harvard Medical School, a faculty member in the Computational Health Informatics Program at Boston Children’s Hospital, and an associate at the Harvard Institute for Applied and Computational Sciences. Mauricio enjoys working with clinicians in the design of decision-making support tools.

Mauricio is a physicist and applied mathematician with expertise in mathematical modeling and scientific computing. He has worked in multiple research areas frequently analyzing big data sets to understand and predict the behavior of complex systems. His research modeling population growth patterns has informed policy makers in Mexico and Texas. His research in numerical analysis and computational fluid dynamics has been used to improve models of coastal floods due to hurricanes, and to improve the performance of global atmospheric chemistry models. In recent years, his main interest has been to develop mathematical models to improve healthcare. Specifically, he has leveraged information from big data sets from Internet-based services (such as Google, Twitter, Flu Near You, Weather) and electronic health records (EHR) to predict disease incidence in multiple locations worldwide and to predict outcomes in hospitalized patients. Dr. Santillana has advised the CDC and the White House on the development of population-wide disease forecasting tools.

Mauricio received a B.S. in physics with highest honors from the Universidad Nacional Autonoma de Mexico in Mexico City, and a master’s and PhD in computational and applied mathematics from the University of Texas at Austin. Mauricio first joined Harvard as a postdoctoral fellow at the Harvard Center for the Environment and has been a lecturer in applied mathematics at the Harvard SEAS, receiving two awards for excellence in teaching.

PUBLICATIONS

Publications powered by Harvard Catalyst Profiles

  1. Castiñeira D, Schlosser KR, Geva A, Rahmani AR, Fiore G, Walsh BK, Smallwood CD, Arnold JH, Santillana M. Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach. Respir Care. 2020 Sep; 65(9):1367-1377. View abstract
  2. Poirier C, Liu D, Clemente L, Ding X, Chinazzi M, Davis J, Vespignani A, Santillana M. Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models. J Med Internet Res. 2020 08 17; 22(8):e20285. View abstract
  3. Aiken EL, McGough SF, Majumder MS, Wachtel G, Nguyen AT, Viboud C, Santillana M. Real-time estimation of disease activity in emerging outbreaks using internet search information. PLoS Comput Biol. 2020 08; 16(8):e1008117. View abstract
  4. Kogan NE, Clemente L, Liautaud P, Kaashoek J, Link NB, Nguyen AT, Lu FS, Huybers P, Resch B, Havas C, Petutschnig A, Davis J, Chinazzi M, Mustafa B, Hanage WP, Vespignani A, Santillana M. An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time. ArXiv. 2020 Jul 01. View abstract
  5. Chevalier-Cottin EP, Ashbaugh H, Brooke N, Gavazzi G, Santillana M, Burlet N, Tin Tin Htar M. Communicating Benefits from Vaccines Beyond Preventing Infectious Diseases. Infect Dis Ther. 2020 Sep; 9(3):467-480. View abstract
  6. Lu FS, Nguyen AT, Link NB, Lipsitch M, Santillana M. Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches. medRxiv. 2020 Jun 18. View abstract
  7. Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, Wesolowski A, Santillana M, Zhang C, Du X, Yu H, Tatem AJ. Effect of non-pharmaceutical interventions to contain COVID-19 in China. Nature. 2020 09; 585(7825):410-413. View abstract
  8. Dai M, Liu D, Liu M, Zhou F, Li G, Chen Z, Zhang Z, You H, Wu M, Zheng Q, Xiong Y, Xiong H, Wang C, Chen C, Xiong F, Zhang Y, Peng Y, Ge S, Zhen B, Yu T, Wang L, Wang H, Liu Y, Chen Y, Mei J, Gao X, Li Z, Gan L, He C, Li Z, Shi Y, Qi Y, Yang J, Tenen DG, Chai L, Mucci LA, Santillana M, Cai H. Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multicenter Study during the COVID-19 Outbreak. Cancer Discov. 2020 06; 10(6):783-791. View abstract
  9. Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Davis JT, Vespignani A, Santillana M. 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. ArXiv. 2020 Apr 08. View abstract
  10. Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, Grad YH, Grenfell B, Halloran ME, Kraemer MUG, Lipsitch M, Metcalf CJE, Meyers LA, Perkins TA, Santillana M, Scarpino SV, Viboud C, Wesolowski A, Schroeder A. Aggregated mobility data could help fight COVID-19. Science. 2020 04 10; 368(6487):145-146. View abstract
  11. Poirier C, Luo W, Majumder MS, Liu D, Mandl K, Mooring T, Santillana M. The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales. SSRN. 2020 Mar 12; 3552677. View abstract
  12. Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, Wesolowski A, Santillana M, Zhang C, Du X, Yu H, Tatem AJ. Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China. medRxiv. 2020 Mar 06. View abstract
  13. Tideman S, Santillana M, Bickel J, Reis B. Internet search query data improve forecasts of daily emergency department volume. J Am Med Inform Assoc. 2019 12 01; 26(12):1574-1583. View abstract
  14. Schlosser KR, Fiore GA, Smallwood CD, Griffin JF, Geva A, Santillana M, Arnold JH. Noninvasive Ventilation Is Interrupted Frequently and Mostly Used at Night in the Pediatric Intensive Care Unit. Respir Care. 2020 Mar; 65(3):341-346. View abstract
  15. Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health Surveill. 2019 Sep 14; 5(4):e13403. View abstract
  16. Clemente L, Lu F, Santillana M. Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries. JMIR Public Health Surveill. 2019 Apr 04; 5(2):e12214. View abstract
  17. Naveca FG, Claro I, Giovanetti M, de Jesus JG, Xavier J, Iani FCM, do Nascimento VA, de Souza VC, Silveira PP, Lourenço J, Santillana M, Kraemer MUG, Quick J, Hill SC, Thézé J, Carvalho RDO, Azevedo V, Salles FCDS, Nunes MRT, Lemos PDS, Candido DDS, Pereira GC, Oliveira MAA, Meneses CAR, Maito RM, Cunha CRSB, Campos DPS, Castilho MDC, Siqueira TCDS, Terra TM, de Albuquerque CFC, da Cruz LN, Abreu AL, Martins DV, Simoes DSMV, Aguiar RS, Luz SLB, Loman N, Pybus OG, Sabino EC, Okumoto O, Alcantara LCJ, Faria NR. Genomic, epidemiological and digital surveillance of Chikungunya virus in the Brazilian Amazon. PLoS Negl Trop Dis. 2019 03; 13(3):e0007065. View abstract
  18. Lu FS, Hattab MW, Clemente CL, Biggerstaff M, Santillana M. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nat Commun. 2019 01 11; 10(1):147. View abstract
  19. Lipsitch M, Santillana M. Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic. Curr Top Microbiol Immunol. 2019; 424:59-74. View abstract
  20. Majumder MS, Cohn EL, Santillana M, Brownstein JS. Estimation of Pneumonic Plague Transmission in Madagascar, August-November 2017. PLoS Curr. 2018 Nov 01; 10. View abstract
  21. Baltrusaitis K, Brownstein JS, Scarpino SV, Bakota E, Crawley AW, Conidi G, Gunn J, Gray J, Zink A, Santillana M. 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. BMC Infect Dis. 2018 08 15; 18(1):403. View abstract
  22. Santillana M, Tuite A, Nasserie T, Fine P, Champredon D, Chindelevitch L, Dushoff J, Fisman D. . Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model," Farr's Law" and SIR Compartmental Difference Equation Models. Infectious Disease Modelling. 2018; 3(1):1-12. View abstract
  23. MacFadden DR, McGough SF, Fisman D, Santillana M, Brownstein JS. Antibiotic Resistance Increases with Local Temperature. Nature Climate Change. 2018; (8):510-514. View abstract
  24. MacFadden DR, McGough SF, Fisman D, Santillana M, Brownstein JS. Antibiotic Resistance Increases with Local Temperature. Nat Clim Chang. 2018 Jun; 8(6):510-514. View abstract
  25. Santillana M, Tuite A, Nasserie T, Fine P, Champredon D, Chindelevitch L, Dushoff J, Fisman D. Relatedness of the incidence decay with exponential adjustment (IDEA) model, "Farr's law" and SIR compartmental difference equation models. Infect Dis Model. 2018; 3:1-12. View abstract
  26. Lu FS, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, Hawkins J, Brownstein J, Conidi G, Gunn J, Gray J, Zink A, Santillana M. Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis. JMIR Public Health Surveill. 2018 Jan 09; 4(1):e4. View abstract
  27. Brownstein JS, Chu S, Marathe A, Marathe MV, Nguyen AT, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, Tizzoni M, Vespignani A, Vullikanti AKS, Wilson ML, Zhang Q. Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches. JMIR Public Health Surveill. 2017 Nov 01; 3(4):e83. View abstract
  28. Kluberg SA, McGinnis DP, Hswen Y, Majumder MS, Santillana M, Brownstein JS. County-level assessment of United States kindergarten vaccination rates for measles mumps rubella (MMR) for the 2014-2015 school year. Vaccine. 2017 11 07; 35(47):6444-6450. View abstract
  29. Yang S, Kou SC, Lu F, Brownstein JS, Brooke N, Santillana M. Advances in using Internet searches to track dengue. PLoS Comput Biol. 2017 Jul; 13(7):e1005607. View abstract
  30. Yang S, Santillana M, Brownstein JS, Gray J, Richardson S, Kou SC. Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infect Dis. 2017 05 08; 17(1):332. View abstract
  31. Baltrusaitis K, Santillana M, Crawley AW, Chunara R, Smolinski M, Brownstein JS. Determinants of Participants' Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System. JMIR Public Health Surveill. 2017 Apr 07; 3(2):e18. View abstract
  32. McGough SF, Brownstein JS, Hawkins JB, Santillana M. Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. PLoS Negl Trop Dis. 2017 01; 11(1):e0005295. View abstract
  33. Schlosser K, Smallwood C, Arnold J, Lee G, Priebe G, Walsh B, Santillana M. 1015: IDENTIFICATION OF PEDIATRIC VENTILATOR-ASSOCIATED CONDITIONS USING CONTINUOUS VENTILATOR DATA. Crit Care Med. 2016 Dec; 44(12 Suppl 1):330. View abstract
  34. Smallwood C, Walsh B, Rettig J, Thompson J, Santillana M, Arnold J. 955: A MACHINE-LEARNING ALGORITHM FOR OXYGENATION RESPONSE PREDICTION IN MECHANICALLY VENTILATED CHILDREN. Crit Care Med. 2016 Dec; 44(12 Suppl 1):315. View abstract
  35. Walsh B, Smallwood C, Rettig J, Santillana M, Arnold J. 949: DEVELOPMENT OF HEART, RESPIRATORY RATE, AND OXYGEN SATURATION PERCENTILE CURVES IN CHILDREN. Crit Care Med. 2016 Dec; 44(12 Suppl 1):313. View abstract
  36. Johansson MA, Reich NG, Hota A, Brownstein JS, Santillana M. Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Sci Rep. 2016 Sep 26; 6:33707. View abstract
  37. Santillana M. Editorial Commentary: Perspectives on the Future of Internet Search Engines and Biosurveillance Systems. Clin Infect Dis. 2017 01 01; 64(1):42-43. View abstract
  38. Majumder MS, Santillana M, Mekaru SR, McGinnis DP, Khan K, Brownstein JS. Utilizing Nontraditional Data Sources for Near Real-Time Estimation of Transmission Dynamics During the 2015-2016 Colombian Zika Virus Disease Outbreak. JMIR Public Health Surveill. 2016 Jun 01; 2(1):e30. View abstract
  39. Santillana M, Nguyen AT, Louie T, Zink A, Gray J, Sung I, Brownstein JS. Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance. Sci Rep. 2016 05 11; 6:25732. View abstract
  40. Yang S, Santillana M, Kou SC. Accurate estimation of influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci U S A. 2015 Nov 24; 112(47):14473-8. View abstract
  41. Santillana M, Nguyen AT, Dredze M, Paul MJ, Nsoesie EO, Brownstein JS. Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLoS Comput Biol. 2015 Oct; 11(10):e1004513. View abstract
  42. Smolinski MS, Crawley AW, Baltrusaitis K, Chunara R, Olsen JM, Wójcik O, Santillana M, Nguyen A, Brownstein JS. Flu Near You: Crowdsourced Symptom Reporting Spanning 2 Influenza Seasons. Am J Public Health. 2015 Oct; 105(10):2124-30. View abstract
  43. Majumder MS, Kluberg S, Santillana M, Mekaru S, Brownstein JS. 2014 ebola outbreak: media events track changes in observed reproductive number. PLoS Curr. 2015 Apr 28; 7. View abstract
  44. Nagar R, Yuan Q, Freifeld CC, Santillana M, Nojima A, Chunara R, Brownstein JS. A case study of the New York City 2012-2013 influenza season with daily geocoded Twitter data from temporal and spatiotemporal perspectives. J Med Internet Res. 2014 Oct 20; 16(10):e236. View abstract
  45. Santillana M, Nsoesie EO, Mekaru SR, Scales D, Brownstein JS. Using clinicians' search query data to monitor influenza epidemics. Clin Infect Dis. 2014 Nov 15; 59(10):1446-50. View abstract
  46. Santillana M, Zhang DW, Althouse BM, Ayers JW. What can digital disease detection learn from (an external revision to) Google Flu Trends? Am J Prev Med. 2014 Sep; 47(3):341-7. View abstract
  47. Gluskin RT, Johansson MA, Santillana M, Brownstein JS. Evaluation of Internet-based dengue query data: Google Dengue Trends. PLoS Negl Trop Dis. 2014 Feb; 8(2):e2713. View abstract
  48. Calo VM, Collier N, Gehre M, Jin B, Radwan H, Santillana M. Gradient-based estimation of Manning’s friction coefficient from noisy data. Journal of Computational and Applied Mathematics. 2013; (238):1–13. View abstract
  49. Santillana M. Quantifying the loss of information in source attribution problems using the adjoint method in global models of atmospheric chemical transport. arXiv preprint arXiv:1311.6315. 2013. View abstract
  50. Santillana M, Dawson C. A numerical approach to study the properties of solutions of the diffusive wave approximation of the shallow water equations. Computational Geosciences. 2010; 1(14):31-53. View abstract
  51. Santillana M, Dawson C. A local discontinuous Galerkin method for a doubly nonlinear diffusion equation arising in shallow water modeling. Computer Methods in Applied Mechanics and Engineering. 2010; 23(199):1424–1436.. View abstract
  52. Zhan BF, Tapia Silva FO, Santillana M. Estimating small-area population growth using geographic-knowledge-guided cellular automata. International Journal of Remote Sensing. 2010; 21(31):5689–5707. View abstract
  53. Santillana M, Le Sager P, Jacob DJ, Brenner MP. An adaptive reduction algorithm for efficient chemical calculations in global atmospheric chemistry models. Atmospheric Environment. 2010; 35(44):4426–4431. View abstract
  54. Alonso R, Santillana M, Dawson C. On the diffusive wave approximation of the shallow water equations. European Journal of Applied Mathematics. 2008; 05(19):575–606. View abstract