Harvard University homepage
HARVARD.EDU

Publications

(Google Scholar Profile)

Peer Reviewed

(* indicates a senior or co-senior author publication, ^ co-first author)

(Submitted/ In review)

78. C. Kusiaka, M. Santillana Justin Lessler, Sopon Iamsirithaworn, N G Reich. Real-time dengue forecasting in Thailand: a comparison of penalized regression approaches using internet search data. Submitted.

(In press)

77*. C. Poirier, Y. Hswen, G. Bouzille, M. Cuggia, A. Lavenu, J. S. Brownstein, T. Brewer, M. Santillana Influenza forecasting for the French regions by using EHR, web and climatic data sources with an ensemble approach. PLoS One. In press

76*. McGough S, Kutz NJ, Clemente LC, M. Santillana. Combining weather patterns and cycles of population susceptibility to forecast dengue fever epidemic years in Brazil: a dynamic, ensemble learning approach. Journal of the Royal Society Interface. In press

75*. E. L. Aiken, A. T. Nguyen, M. Santillana Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions. Science Advances. In press

74*. Lu FS, Nguyen AT, Link N, Molina M, Davis JT, Chinazzi M, Xiong X, Vespignani A, Lipsitch M, Santillana M. Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: four complementary approaches. PLoS Computational Biology . In Press.

73*. Castro LA, Generous N, Luo W, y Piontti AP, Martinez K, Gomes MFC, Osthus D, Fairchild G, Ziemann A, Vespignani A, et al. Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil. PLoS Neglected Tropical Diseases. In Press.

2021

72*. de Salazar P, Link N, Lamarca K, Santillana M. High coverage COVID-19 mRNA vaccination rapidly controls SARS-CoV-2 transmission in Long-Term Care Facilities. Research Square (Nature Portfolio)– Preprint. 2021

71. Perlis RH, Ognyanova K, Santillana M., Baum MA, Lazer D, Druckman J, Volpe JD. Association of Acute Symptoms of COVID-19 and Symptoms of Depression in Adults . JAMA Network Open. 2021;4 (3) :e213223.

70*. de Salazar PM, Lu F, Hay JA, Gomez-Barroso D, Fernandez-Navarro P, Martinez EV, Astray-Mochales J, Amillategui R, Garcia-Fulgueiras A, Chirlaque MD, et al. Near real-time surveillance of the SARS-CoV-2 epidemic with incomplete data. medRxiv. 2021.

69*. Mena G, Martinez PP, Mahmud AS, Marquet PA, Buckee CO, Santillana M . Socioeconomic status determines COVID-19 incidence and related mortality in Santiago, Chile. Science. 2021 :eabg5298.

68. Kiang MV, Santillana M ^, Chen JT, Onnela J-P, Krieger N, Engø-Monsen K, Ekapirat N, Areechokchai D, Maude R, Buckee CO. Incorporating human mobility data improves forecasts of Dengue fever in Thailand . Scientific Reports. 2021;11 (923).

67*. Kogan NE, Clemente L, Liautaud P, Kaashoek J, Link NB, Nguyen AT, Lu FS, Huybers P, Resch B, Havas C, …, Santillana M . An Early Warning Approach to Monitor COVID-19 Activity with Multiple Digital Traces in Near Real-Time . Science Advances. 2021;7 (10).

2020

66*. Koplewitz G, Lu F, Clemente L, Buckee C, Santillana M . Predicting Dengue Incidence Leveraging Internet-Based Data Sources. A Case Study in 20 cities in Brazil . medRxiv. 2020;2020.10.21.20210948.

65. Hanage WP, Testa C, Chen JT, David L, Pechter E, Seminario P, Santillana M , Krieger N. COVID-19: US Federal accountability for entry, spread, and inequities – lessons for the future . European Journal of Epidemiology [Internet]. 2020.

64*. Patel B, Sperotto F, Molina M, Kimura S, Delgado M, Santillana M , Kheir JN. Avoidable serum potassium testing in the cardiac intensive care unit: development and testing of a machine learning model. Pediatric Critical Care Medicine. 2020;22 (4).

63. Emma-Pascale Chevalier-Cottin, Hayley Ashbaugh, Nicholas Brooke, Gaetan Gavazzi, Mauricio Santillana , Nansa Burlet, Myint Tin Tin Htar. Communicating Benefits from Vaccines Beyond Preventing Infectious Diseases . Infectious Diseases and Therapy. 2020;9 :467–480.

62. Buckee CO, Balsari S, Chan J, Crosas M, Dominici F, Gasser U, Grad YH, Grenfell B, Halloran ME, Kraemer MUG, et al. Aggregated mobility data could help fight COVID-19 . Science. 2020;368 (6487) :145-146.

61*. Dai M-Y, Liu D, Liu M, Zhou F-X, .., Mucci LA, Santillana M, Cai H-B. Patients with Cancer Appear More Vulnerable to SARS-CoV-2: A Multi-Center Study During the COVID-19 Outbreak. Cancer Discovery. 2020;DOI: 10.1158/2159-8290.CD-20-0422.

60. Lai S, Ruktanonchai NW, Zhou L, Prosper O, Luo W, Floyd JR, Wesolowski A, Santillana M, Zhang C, Du X, et al. Effect of non-pharmaceutical interventions for containing the COVID-19 outbreak in China. Nature. 2020;https://doi.org/10.1038/s41586-020-2293-x.

59*. Kaashoek J, M. Santillana. 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 . SSRN. 2020.

58*. Poirier C, Luo W, Majumder MS, Liu D, Mandl K, Mooring T, M. Santillana. The Role of Environmental Factors on Transmission Rates of the COVID-19 Outbreak: An Initial Assessment in Two Spatial Scales . Scientific Reports. 2020;10 :17002.

57*.Liautaud P, Huybers P, M. Santillana. Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United States . arXiv. 2020.

56*.Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Davis JT, Vespignani A, M. Santillana. A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models . Journal of Medical Internet Research. 2020;22 (8).

55*.Fred S. Lu, Andre T. Nguyen, Nick Link, M. Santillana. Estimating the Prevalence of COVID-19 in the United States: Three Complementary Approaches. MedRxiv

54*. W. Luo, M. S. Majumder, D. Liu, C. Poirier, K. D. Mandl, M. Lipsitch, M. Santillana. The role of absolute humidity on transmission rates of the COVID-19 outbreak. To appear in MedRxiv

53*. McGough S, MacFadden DR, Hattab MW, Mølbak K, Santillana M.. Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000-2016 . Eurosurveillance. Volume 25, Issue 45, 2020.

52. C. Viboud and M. Santillana. Fitbit-informed influenza forecasts. Lancet Digital Health. Vol 2. Issue 2. 2020

51. Shen L, Jacob DJ, Santillana M, Wang X, Chen W. An adaptive method for speeding up the numerical integration of chemical mechanisms in atmospheric chemistry models. Geoscientific Model Development . 2020;13 :2475–2486

50*. E. L Aiken, S. F McGough, M. S Majumder, G. Wachtel, A. T Nguyen, C. Viboud, M. Santillana Real-time Estimation of Disease Activity in Emerging Outbreaks using Internet Search Information. PLoS Computational Biology. 2020

49*. Castiñeira D, Schlosser K, Geva A, Rahmani A, Fiore G, Walsh BK, Smallwood CD, Arnold JH, M. Santillana. Finding Value in Continuous in Time Vital-Sign Information Using a Scalable Data Driven Machine Learning Approach. A pilot study in a Pediatric Intensive Care Unit. Respiratory Care, 65. 2020

2019

48*.Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, M. Santillana. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation . JMIR Public Health Surveillance. 2019;5 (4) :e13403.

47*. Mejia K, Viboud C, M. Santillana. Leveraging Google Search Data to Track Influenza Outbreaks in Africa. Gates Open Research. 2019;3 :1653

46. Lipsitch M and Santillana M. Enhancing Situational Awareness to Prevent Infectious Disease Outbreaks from Becoming Catastrophic. In: Inglesby T Global Catastrophic Biological Risk. Springer, Berlin, Heidelberg. ; 2019.

45. Naveca FG, Claro I, Giovanetti M, Jesus JG, Javier J, Iani FCM, do Nascimento VA, Souza VC, Silveira PP, Lourenco J, et al. Chikungunya virus outbreak in the Amazon region: replacement of the Asian genotype by an ECSA lineage. PLoS Neglected Tropical Diseases. 2019;13 (3) :e0007065.

44. Schlosser KR, Fiore GA, Smallwood CD, Griffin J, Geva A, Santillana M, Arnold JH. Noninvasive Ventilation Is Interrupted Frequently and Mostly Used at Night in the Pediatric Intensive Care Unit. Respiratory Care. 2019;64 (9)

43. Tideman S, Santillana M, Bickel J, Reis B Internet search query data improves forecasts of daily emergency department volume. Journal of the American Medical Informatics Association. 2019;ocz154.

42*. Clemente LC, Lu F, Santillana M. Improved real-time influenza surveillance using Internet search data in eight Latin American countries. JMIR Public Health Surveillance. 2019;5 (2) :e12214.

41*. Lu F, Hattab M, Clemente L, Santillana M. Improved state-level influenza activity nowcasting in the United States leveraging Internet-based data sources and network approaches via ARGONet. Nature Communications. 2019;10 (147).

2018

40. Majumder MS, Cohn EL, Santillana M, Brownstein JS. Estimation of Pneumonic Plague Transmission in Madagascar, August–November 2017. PLOS Currents Outbreaks. 2018 Nov 1. Edition 1.

39*. Baltrusaitis K, Brownstein JS, Scarpino SV, Bakota E, Crawley A, Biggerstaff M, 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 Infectious Diseases. 2018;18 (403)

38*. MacFadden DR, McGough SF, Fisman D, Santillana M, Brownstein JS. Antibiotic Resistance Increases with Local Temperature Nature Climate Change 8 (2018), pp 510-514.

37. M. Santillana, A. Tuite, T. Nasserie, P. Fine, D. Champredon L. Chindelevitch, J. Dushoff, D. Fisman. Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model, “Farr’s Law” and Compartmental Difference Equation SIR Models. Infectious Disease Modeling Volume 3, 2018, Pages 1-12 (Preprint in ArXiv about Relatedness of the Incidence Decay with Exponential Adjustment (IDEA) Model, “Farr’s Law” and Compartmental Difference Equation SIR Models)

36*. Lu F, Hou S, Baltrusaitis K, Shah M, Leskovec J, Sosic R, Hawkins J, Brownstein JS, Conidi G, Gunn J, …, Santillana M. Accurate influenza monitoring and forecasting in the Boston metropolis using novel Internet data streams. Journal of Medical Internet Research. 2018;4 (1) :e4.7

2017

35. Marathe A, Brownstein JS, Chu S, Marathe MV, Nguyen AT, Paolotti D, Perra N, Perrotta D, Santillana M, Swarup S, et al. Combining Participatory Influenza Surveillance with Modeling and Forecasting. JMIR Public Health Surveillance . 2017;3 (4) :e83

34. 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;35 :6444-6450.

33*. S. Yang, S. C. Kou, F. Lu, J.S. Brownstein, N. Brooke, M. Santillana. Advances in the use of Google searches to track dengue in Mexico, Brazil, Thailand, Singapore and Taiwan. PLoS Computational Biology 13 (7), e1005607

32. S. Yang, M. Santillana, J.S. Brownstein, J. Gray, S. Richardson, S. C Kou. Using electronic health records and Internet search information for accurate influenza forecasting. BMC Infectious Diseases 2017; 17:332

31. K. Baltrusaitis, M. Santillana, A. Crawley, R. Chunara, M. Smolinski, J. Brownstein. Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance. JMIR Public Health Surveillance. 2017;3 (3) :e18

30*. S.F. McGough, J.S. Brownstein, J. Hawkins, M. Santillana. Forecasting Zika Incidence in the 2016 Latin America Outbreak Combining Traditional Disease Surveillance with Search, Social Media, and News Report Data. PLoS Neglected Tropical Diseases 11(1): e0005295.

29*. M. Santillana. Perspectives on the future of Internet search engines and biosurveillance systems. Clinical Infectious Diseases. 64 (1): 42-43. (2017)

2016

28*. Schlosser K, Smallwood C, Arnold J, Lee G, Priebe G, Walsh B, M. Santillana 1015: Identification of pediatric ventilator-associated conditions using continuous ventilator data. Critical Care Medicine, 2016 Dec; Volume 44, Issue 12, pp:330.

27. Smallwood C, Walsh B, Rettig J, Thompson J, M. Santillana, Arnold J. 955: A machine-learning algorithm for oxygenation response prediction in mechanically ventilated children. Critical Care Medicine, 2016 Dec; Volume 44, Issue 12, pp:315.

26. Walsh B, Smallwood C, Rettig J, M. Santillana, Arnold J. 949: Development of heart, respiratory rate, and oxygenation saturation percentile curves in children. Critical Care Medicine, 2016 Dec; Volume 44, Issue 11pp:313.

25*. M.A. Johansson, N.G. Reich, A. Hota, J. S. Brownstein, and M. Santillana. Evaluating the performance of infectious disease forecasts: A comparison of climate-driven and seasonal dengue forecasts for Mexico. Scientific Reports 6, 33707 (2016)

24. Majumder MS, M. Santillana, 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 Surveillance (2016); 2(1):e30

23. M. Santillana, A.T. Nguyen, T. Louie, A. Zink, J. Gray, I. Sung, and J.S. Brownstein. Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance. Scientific Reports 6, 25732 (2016) (Preprint in ArXiv about Cloud-based Electronic Health Records for Real-time, Region-specific Influenza Surveillance)

• News coverage: HealthData management, Science Daily, Politico, EurekAlert!, News Medical, MedCity News, Vector (BCH), Insights Samsung, UPI Health news.

22. M. Santillana, L. Zhang, and R. Yantosca. Estimating numerical errors due to operator splitting in global atmospheric chemistry models. Journal of Computational Physics. 305 (2016) 372–386. (Preprint in ArXiv about Estimating numerical errors due to operator splitting in global atmospheric chemistry models)

2015

21*. S. Yang, M. Santillana, and S. C. Kou. Accurate estimation of influenza epidemics using Google search data via ARGO. Proceedings of the National Academy of Sciences, 112.47, pp: 14473-14478, 2015. (Supplementary material about Accurate estimation of influenza epidemics using Google search data via ARGO)

• News coverage: Harvard Gazette, Rue89 (France), Deutschlandfunk (Germany), Sinc (Spain), Arstechnica.

20. M. Santillana, A. T. Nguyen, M. Dredze, M.J. Paul, E. Nsoesie, and J. S. Brownstein. Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance. PLOS Computational Biology. 11(10): e1004513, Oct, 2015.

• News coverage: NPR, Harvard Medical School , Boston Magazine, AAAS, MedTech, Health IT Analytics

19. M. S. Smolinski, A. W. Crawley, K. Baltrusaitis, R. Chunara, J. M. Olsen, O. Wojick, M. Santillana, A. T. Nguyen, J. S. Brownstein. Flu Near You: Crowdsourced Symptom Reporting Spanning Two Influenza Seasons. American Journal of Public Health. 105 (10), 2124-2130, 2015.

18. M. S. Majumder, S. A. Kluberg, M. Santillana, S. R. Mekaru, and J. S. Brownstein. 2014 Ebola Outbreak: Media events track changes in observed reproductive number. PLoS Currents: Outbreaks. 2015 Apr 28, Edition 1.

2014

17. R. Nagar, Q. Yuan, C. Freifeld, M. Santillana, A. Nojima, R. Chunara, and J. S. Brownstein. A Case Study of the New York City 2012-2013 Influenza Season with Daily, Geocoded Twitter Data from Temporal and Spatiotemporal Perspectives. Journal of Medical Internet Research, 16(10): e236, Nov 2014.

16. M. Santillana, E. O. Nsoesie, S. R. Mekaru, D. Scales, and J. S. Brownstein. Using Clinician’s Search Query Data to Monitor Influenza Epidemics. Clinical Infectious Diseases, 59 (10), pp:1446-1450, 2014.

• News coverage: UpToDate.

15. M. Santillana, D.W. Zhang, B.M. Althouse, and J.W. Ayers. What Can Digital Disease Detection Learn from (an External Revision to) Google Flu Trends?. American Journal of Preventive Medicine. Volume 47, Issue 3, pp: 341–347, Sep 2014.

• News coverage: The Guardian (UK), Health Data Management.

14. R.T. Gluskin, M.A. Johansson, M. Santillana, and J.S. Brownstein. Evaluation of internet-based dengue query data: Google Dengue Trends. PLoS Neglected Tropical Diseases, 8(2): e2713, Feb 2014.

2013 and earlier

13. V.M. Calo, N. Collier, M. Gehre, B. Jin, H. Radwan, and M. Santillana. Gradient-based estimation of Manning’s friction coeficient from noisy data. Journal of Computational and Applied Mathematics, 238, pp 1–13, Jan 2013.

12. H. G. Radwan, P. Vignal, N. Collier, L. Dalcin, M. Santillana, and V.M. Calo. Convergence rates for the Diffusive Shallow Water Equations (DSW) using higher order polynomials. Journal of the Serbian Society of Computational Mechanics, Vol. 6, No. 1, Nov 2012

11. M. Santillana, P. Le Sager, D. J. Jacob, and M. P. Brenner. An adaptive reduction algorithm for efficient chemical calculations in global atmospheric chemistry models. Atmospheric Environment. Volume 44, Issue 35, pp 4426-4431, Nov 2010.

10. M. Santillana and C. Dawson. A local discontinuous Galerkin method for a doubly nonlinear diffusion equation arising in shallow water modeling. Computer Methods in Applied Mechanics and Engineering. Volume 199, Issue 23, pp. 1424-1436, Apr 2010.

9. B. Zhan, O. Tapia, and M. Santillana. Estimating Small Area Population Growth Using Geographic Knowledge Guided Cellular Automata. International Journal of Remote Sensing. Volume 31, Issue 21, 5689, Jul 2010.

8. M. Santillana and C. Dawson. A numerical approach to study the properties of solutions of the diffusive wave approximation of the Shallow Water equations. Computational Geosciences. Volume 14, Issue 1, pp 31-53. Jan 2010.

7. R. Alonso, M. Santillana, and C. Dawson. On the diffusive wave approximation of the Shallow Water equations. European Journal of Applied Mathematics. Volume 19, Issue 5, Oct 2008, pp 575-606.

Theses, Technical Reports, and Manuscripts in Peer-reviewed Proceedings

6. M. Santillana. PhD. Dissertation: Analysis and Numerical Simulation of the Diffusive Wave Approximation of the Shallow Water Equations. Advisor: Clint Dawson. 7/2008

5. M. Santillana. Bachelor’s Degree Thesis: From Classical Mechanics to Quantum Mechanics. A variational Perspective. Advisor: Professor Pablo Padilla Longoria. (In Spanish). 7/2001.

4. M. Santillana. Quantifying the loss of information in source attribution problems using the adjoint method in global models of atmospheric chemical transport. 2013.

3. R. Gluskin, M. Santillana, and John Brownstein. Using Google Dengue Trends to Estimate Climate Effects in Mexico. Online Journal of Public Health Informatics . Volume 5, No 1, 2013.

2. R. Alonso, M. Santillana, and C. Dawson. Analysis of the diffusive wave approximation of the Shallow Water equations. ICES Report 07-19, Univ. of Texas, Austin 9/2007.

1. M. Santillana and F. Serrano. Calibration and validation of a CA based model using an evolutionary algorithm for urban development simulation. A case study in Mexico City. Proceedings of the Ninth International Conference on Computers in Urban Planning and Urban Management (CUPUM 2005). University College London, London, UK. 6/2005.

Selected Major Presentations