Assistant Professor

Mauricio Santillana

Assistant Professor in the Department of Epidemiology

Epidemiology

Mauricio.SantillanaGuzman@childrens.harvard.edu

Other Positions

Assistant Professor of Pediatrics

Pediatrics

Boston Children's Hospital

Lecturer on Applied Mathematics

SEAS

Harvard University

Associate in Computer Science (IACS)

SEAS

Harvard University

Links

Mauricio Santillana Catalyst Profile

Overview

My current research utilizes mathematical concepts of machine learning for:
(a) the design and implementation of novel ways to track epidemic outbreaks around the Globe in real-time, using data from: Google searches, Twitter microblogs, Electronic Health Records, and news reports, and
(b) to improve patient outcomes and reduce hospital costs in Critical Care Medicine.

Bibliography

Avoidable Serum Potassium Testing in the Cardiac ICU: Development and Testing of a Machine-Learning Model.

Patel BB, Sperotto F, Molina M, Kimura S, Delgado MI, Santillana M, Kheir JN.

Pediatr Crit Care Med. 2020 Dec 16. Publish Ahead of Print. PMID: 33332868

COVID-19: US federal accountability for entry, spread, and inequities-lessons for the future.

Hanage WP, Testa C, Chen JT, Davis L, Pechter E, Seminario P, Santillana M, Krieger N.

Eur J Epidemiol. 2020 Nov. 35(11):995-1006. PMID: 33136249

Rates of increase of antibiotic resistance and ambient temperature in Europe: a cross-national analysis of 28 countries between 2000 and 2016.

McGough SF, MacFadden DR, Hattab MW, Mølbak K, Santillana M.

Euro Surveill. 2020 Nov. 25(45). PMID: 33183408

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 KD, Mooring TA, Santillana M.

Sci Rep. 2020 10 12. 10(1):17002. PMID: 33046802

Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.

Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Davis J, Vespignani A, Santillana M.

J Med Internet Res. 2020 Sep 22. 22(9):e23996. PMID: 32960774

Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.

Castiñeira D, Schlosser KR, Geva A, Rahmani AR, Fiore G, Walsh BK, Smallwood CD, Arnold JH, Santillana M.

Respir Care. 2020 Sep. 65(9):1367-1377. PMID: 32879034

Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.

Liu D, Clemente L, Poirier C, Ding X, Chinazzi M, Davis J, Vespignani A, Santillana M.

J Med Internet Res. 2020 08 17. 22(8):e20285. PMID: 32730217

Real-time estimation of disease activity in emerging outbreaks using internet search information.

Aiken EL, McGough SF, Majumder MS, Wachtel G, Nguyen AT, Viboud C, Santillana M.

PLoS Comput Biol. 2020 08. 16(8):e1008117. PMID: 32804932

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, Petutschnig A, Davis J, Chinazzi M, Mustafa B, Hanage WP, Vespignani A, Santillana M.

ArXiv. 2020 Jul 01. PMID: 32676518

Communicating Benefits from Vaccines Beyond Preventing Infectious Diseases.

Chevalier-Cottin EP, Ashbaugh H, Brooke N, Gavazzi G, Santillana M, Burlet N, Tin Tin Htar M.

Infect Dis Ther. 2020 Sep. 9(3):467-480. PMID: 32583334

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