Megan Coffee, MD, PhD

  • Assistant Professor of Population and Family Health


Megan Coffee is an infectious disease physician and researcher, working on mathematical modeling and machine learning approaches to predict, identify and respond to infectious disease outbreaks in resource limited locations globally. She teaches on infectious disease outbreaks globally in resource limited settings with Prof Rachel Moresky through the Department of Population and Family Health. She received her undergraduate and medical degrees at Harvard University. She obtained her doctoral degree in mathematical modeling and epidemiology of infectious diseases from Oxford University, working on computer models of HIV spread in southern Africa, looking at migration and mobility. She completed her residency in internal medicine at Massachusetts General Hospital and her fellowship at UCSF. She is board certified in infectious diseases and internal medicine and is a licensed doctor in multiple states, including NY where she lives. She has worked globally on infectious diseases. She was the director of a tuberculosis ward at the main general hospital in Port-Au-Prince, Haiti for 4 years and continues to help TB and HIV patients with telemedicine home-based care, through the non-profit Ti Kay, which she runs with a Haitian and expat team. She was the coordinator in charge of multiple Ebola Units in Sierra Leone with the International Rescue Committee (IRC). She then was the IRC West Africa Health Advisor and later as a Communicable Disease Advisor working with outbreaks and infectious diseases in many countries. She has worked as a part of WHO, CDC, and UN working groups and meetings regarding infectious disease epidemics, including on antimicrobial resistance, Ebola, cholera, and vaccines in emergencies. She sees patients clinically at Bellevue Hospital in New York City and is a clinical assistant professor in infectious diseases at NYU Grossman School of Medicine. She is involved in research on computer-based tools and artificial intelligence for best responding to epidemics, including working on social media and vaccine hesitancy, AI based radiologic tools for tuberculosis classification and prediction and clinical severity tools for COVID and other infections.

Academic Appointments

  • Assistant Professor of Population and Family Health

Credentials & Experience

Education & Training

  • BA, Harvard University
  • PhD, Oxford University
  • MD, Harvard University

Committees, Societies, Councils

Editorial Boards

PLOS Global Health


Selected Publications

Bari A, Heymann M, Cohen RJ, Zhao R, Szabo L, Vasandani SA, Khubchandani A, DiLorenzo M, Coffee M, Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms, Clinical Infectious Diseases, Volume 74, Issue Supplement_3, 15 May 2022, Pages e4–e9,

Bauch CT, Lloyd-Smith J, Coffee MP, Galvani AP. Dynamically modeling SARS and other newly emerging respiratory illnesses: past, present, and future. Epidemiology 2005 Nov 1:16(6) 791-801

Ekins S, Freundlich JS, Coffee M A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus. F1000Res. 2014 Nov 14 3:277 2014.

Lewis A, Mahmoodi E, Zhou Y, Coffee M, Sizikova E. Improving Tuberculosis (TB) Prediction Using Synthetically Generated Computed Tomography (CT) Images; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2021, pp. 3265-3273

Bari A, Khubchandani A, Wang J, Heymann M, Coffee M “COVID-19 Early-Alert Signals using Human Behavior Alternative Data” Social Network Analysis and Mining (SNAM) 11: 18 4 February 2021

Gautier L, Houngbedji KA, Uwamaliya J, Coffee M. Use of a community-led prevention strategy to enhance behavioral changes towards Ebola virus disease: a qualitative case study in western Cote d’Ivoire. Global Health Research and Policy. Global Health Research and Policy. Dec 22 20172:35.

Crowe SJ, Maenner MJ, Kuah S, Erickson BR, Coffee M, Knust B, et al. Prognostic indicators for Ebola patient survival. Emerg Infect Dis. 2016 Feb

Coffee M, Garnett GP, Mlilo M, Voeten H, Chandiwana S, Gregson S. Patterns of movement and risk of HIV in rural Zimbabwe. Journal of Infectious Disease. 2005 Feb 1;191(Suppl 1):S159-67.

Coffee MP, Lurie MN, Garnett GP. Modelling the impact of migration on the HIV epidemic in South Africa. AIDS. 2007 Jan 30;21(3):343-50.

Jiang X, Coffee M, Bari A, Wang J, Jiang X, Huang J, Shi J, Dai J, Cai J, Zhang T, Wu Z, He G, Huang Y. “Towards an Artificial Intelligence Framework for Data-Driven Prediction of Coronavirus Clinical Severity” Computer, Materials, & Continua. 30 March 2020

Global Health Activities

International Rescue Committee, Nigeria, Sierra Leone, Cote d'Ivoire, Liberia, Kenya, Uganda, Thailand, Myanmar, Lebanon, Bangladesh and others

Ti Kay, Haiti: Organization provides telemedicine based home care and outreach for those living with or at risk for TB, HIV, or COVID, with a team built with years of experience and including many former patients.