Sarah Kramer

Postdoctoral Researcher




Sarah Kramer is a postdoctoral research scientist at the Max Planck Institute for Infection Biology, using mathematical models to understand seasonal drivers of vaccine response and infectious disease transmission. She recently received her PhD in Environmental Health Sciences from Columbia University’s Mailman School of Public Health, where her work focused on the generation and evaluation of retrospective and real-time forecasts of influenza. Her interests include drivers of spatial and temporal patterns in infectious disease transmission, zoonotic and other emerging infectious diseases, and the use of mathematical modeling methods to inform public health practice.

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Research/Topics of Interest

Apply models to public health
Climate/environmental drivers
Compartmental models
Network models
Public health application of models
Spatial transmission patterns
Statistical models

Pathogens/Diseases of Main Interest/Expertise

Respiratory diseases
Season influenza
Vaccine-preventable diseases

Countries of Work/Collaboration




Domenech de Cellès M, Goult E, Casalegno JS, Kramer SC. (2022). The pitfalls of inferring virus-virus interactions from co-detection prevalence data: application to influenza and SARS-CoV-2. Proceedings. Biological sciences, 289(1966)

Yuan H, Kramer SC, Lau EHY, Cowling BJ, Yang W. (2021). Modeling influenza seasonality in the tropics and subtropics. PLoS computational biology, 17(6)

Biggerstaff M, Dahlgren FS, Fitzner J, George D, Hammond A, Hall I, Haw D, Imai N, Johansson MA, Kramer S, McCaw JM, Moss R, Pebody R, Read JM, Reed C, Reich NG, Riley S, Vandemaele K, Viboud C, Wu JT. (2020). Coordinating the real-time use of global influenza activity data for better public health planning. Influenza and other respiratory viruses, 14(2)

Kramer SC, Pei S, Shaman J. (2020). Forecasting influenza in Europe using a metapopulation model incorporating cross-border commuting and air travel. PLoS computational biology, 16(10)

Kramer SC, Shaman J. (2019). Development and validation of influenza forecasting for 64 temperate and tropical countries. PLoS computational biology, 15(2)

Morita H, Kramer S, Heaney A, Gil H, Shaman J. (2018). Influenza forecast optimization when using different surveillance data types and geographic scale. Influenza and other respiratory viruses, 12(6)

Doms C, Kramer SC, Shaman J. (2018). Assessing the Use of Influenza Forecasts and Epidemiological Modeling in Public Health Decision Making in the United States. Scientific reports, 8(1)

Kramer SC, Bansal S. (2015). Assessing the use of antiviral treatment to control influenza. Epidemiology and Infection, 143(8)

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