Thomas McAndrew is a faculty member in the Department of Biostatistics and Health Data Science in the College of Health at Lehigh University. He is an expert in the mathematical modeling of infectious diseases. The aim of his research is to improve the predictive power of infectious disease models by augmenting surveillance data with human judgment. In addition, McAndrew studies the impact of forecasts on the public health decision making process. The overarching goal of his work is to use mathematics to improve the health and well being of others. In line with the mission of MIDAS and in service to public health, McAndrew generates real-time forecasts of influenza at federal and state level to support public health decision makers.
He/him/his
United States
Midas Coordination Center Urgent Grant Program – New Initiatives
People: Thomas McAndrew
2020 – 2021 Continue Reading Aggregating computational models and human judgment for more accurate forecasts of the US COVID-19 outbreak
National Institutes of Health, National Institute of General Medical Sciences
People: Thomas McAndrew, Nicholas Reich, Teresa Yamana, Evan Ray
2016 – 2031 Continue Reading Methods for real-time forecasting and inference during infectious disease outbreaks
Hoyt G, Bergren E, String G, McAndrew T. (2026). Strength in Synthesis: Human Expertise and Computational Modeling as Complementary Public Health Investments. American journal of public health, 116(9)
Mcandrew T, Majumder MS, Lover AA, Venkatramanan S, Bocchini P, Besiroglu T, Codi A, Dempsey G, Abbott S, Chevalier S, Bosse NI, Cambeiro J, Braun D. (2026). Assessing human judgment forecasts in the rapid spread of the mpox outbreak: insights and challenges for pandemic preparedness. BMC infectious diseases
McAndrew T, Lover AA, Hoyt G, Majumder MS. (2025). When data disappear: public health pays as US policy strays. The Lancet. Digital health
Mursel S, Davison BD, McAndrew T, Khan B, Bocchini P. (2025). Spatiotemporal Dynamics of Avian Influenza: Understanding Avian Influenza Transmission via Mallard Migration Data. Interdisciplinary perspectives on infectious diseases, (2025)
Wei N, Zhou X, Huang WM, McAndrew T. (2025). A Cluster-Aggregate-Pool (CAP) ensemble algorithm for improved forecast performance of influenza-like illness. Epidemics, (52)
McAndrew T, Gibson GC, Braun D, Srivastava A, Brown K. (2024). Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data. Epidemics, (47)
Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, Rosenfeld R, Shemetov D, Tibshirani RJ, McDonald DJ, Kandula S, Pei S, Yaari R, Yamana TK, Shaman J, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Zhao Z, Rodríguez A, Meiyappan A, Omar S, Baccam P, Gurung HL, Suchoski BT, Stage SA, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Loo SL, McKee CD, Sato K, Smith C, Truelove S, Jung SM, Lemaitre JC, Lessler J, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Gibson GC, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Pastore Y Piontti A, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Suez E, Cojocaru MG, Thommes EW, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Aawar MA, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Ramakrishnan N, Muralidhar N, Reed C, Biggerstaff M, Borchering RK. (2024). Title evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations. Nature communications, 15(1)
Wattanachit N, Ray EL, McAndrew TC, Reich NG. (2023). Comparison of combination methods to create calibrated ensemble forecasts for seasonal influenza in the U.S. Statistics in medicine
Mathis SM, Webber AE, León TM, Murray EL, Sun M, White LA, Brooks LC, Green A, Hu AJ, McDonald DJ, Rosenfeld R, Shemetov D, Tibshirani RJ, Kandula S, Pei S, Shaman J, Yaari R, Yamana TK, Agarwal P, Balusu S, Gururajan G, Kamarthi H, Prakash BA, Raman R, Rodríguez A, Zhao Z, Meiyappan A, Omar S, Baccam P, Gurung HL, Stage SA, Suchoski BT, Ajelli M, Kummer AG, Litvinova M, Ventura PC, Wadsworth S, Niemi J, Carcelen E, Hill AL, Jung SM, Lemaitre JC, Lessler J, Loo SL, McKee CD, Sato K, Smith C, Truelove S, McAndrew T, Ye W, Bosse N, Hlavacek WS, Lin YT, Mallela A, Chen Y, Lamm SM, Lee J, Posner RG, Perofsky AC, Viboud C, Clemente L, Lu F, Meyer AG, Santillana M, Chinazzi M, Davis JT, Mu K, Piontti APY, Vespignani A, Xiong X, Ben-Nun M, Riley P, Turtle J, Hulme-Lowe C, Jessa S, Nagraj VP, Turner SD, Williams D, Basu A, Drake JM, Fox SJ, Gibson GC, Suez E, Thommes EW, Cojocaru MG, Cramer EY, Gerding A, Stark A, Ray EL, Reich NG, Shandross L, Wattanachit N, Wang Y, Zorn MW, Al Aawar M, Srivastava A, Meyers LA, Adiga A, Hurt B, Kaur G, Lewis BL, Marathe M, Venkatramanan S, Butler P, Farabow A, Muralidhar N, Ramakrishnan N, Reed C, Biggerstaff M, Borchering RK. (2023). Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations. medRxiv : the preprint server for health sciences
Codi A, Luk D, Braun D, Cambeiro J, Besiroglu T, Chen E, de Cesaris LEU, Bocchini P, McAndrew T. (2022). Aggregating Human Judgment Probabilistic Predictions of Coronavirus Disease 2019 Transmission, Burden, and Preventive Measures. Open forum infectious diseases, 9(8)
McAndrew T, Cambeiro J, Besiroglu T. (2022). Aggregating human judgment probabilistic predictions of the safety, efficacy, and timing of a COVID-19 vaccine. Vaccine
McAndrew T, Reich NG. (2022). An expert judgment model to predict early stages of the COVID-19 pandemic in the United States. PLoS computational biology, 18(9)
Braun D, Ingram D, Ingram D, Khan B, Marsh J, McAndrew T. (2022). Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study. JMIR public health and surveillance, 8(12)
McAndrew T, Codi A, Cambeiro J, Besiroglu T, Braun D, Chen E, De Cèsaris LEU, Luk D. (2022). Chimeric forecasting: combining probabilistic predictions from computational models and human judgment. BMC infectious diseases, 22(1)
McAndrew T, Majumder MS, Lover AA, Venkatramanan S, Bocchini P, Besiroglu T, Codi A, Braun D, Dempsey G, Abbott S, Chevalier S, Bosse NI, Cambeiro J. (2022). Early human judgment forecasts of human monkeypox, May 2022. The Lancet. Digital health
Codi A, Luk D, Braun D, Cambeiro J, Besiroglu T, Chen E, de Cèsaris LEU, Bocchini P, McAndrew T. (2022). Aggregating human judgment probabilistic predictions of COVID-19 transmission, burden, and preventative measures. ArXiv
McAndrew T, Reich NG. (2021). Adaptively stacking ensembles for influenza forecasting. Statistics in medicine
McAndrew TC, Reich NG. (2020). An expert judgment model to predict early stages of the COVID-19 outbreak in the United States. medRxiv : the preprint server for health sciences
McAndrew T, Wattanachit N, Gibson GC, Reich NG. (2020). Aggregating predictions from experts: a review of statistical methods, experiments, and applications. Wiley interdisciplinary reviews. Computational statistics, 13(2)
McAndrew TC, Guseva EA, Bagrow JP. (2017). Reply & Supply: Efficient crowdsourcing when workers do more than answer questions. PLoS One, 12(8)
McAndrew TC, Danforth CM, Bagrow JP. (2015). Robustness of spatial micronetworks. Physical review. E, Statistical, nonlinear, and soft matter physics, 91(4)
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