Skip to main content


Howerton, Emily; Contamin, Lucie; Mullany, Luke C.; Qin, Michelle; Reich, Nicholas G.; Bents, Samantha; Borchering, Rebecca K.; Jung, Sung-Mok; Loo, Sara L.; & Smith, Claire P., et al. (Preprint). Informing Pandemic Response in the Face of Uncertainty. An Evaluation of the U.S. COVID-19 Scenario Modeling Hub. medRxiv. PMCID: PMC10350156


Our ability to forecast epidemics more than a few weeks into the future is constrained by the complexity of disease systems, our limited ability to measure the current state of an epidemic, and uncertainties in how human action will affect transmission. Realistic longer-term projections (spanning more than a few weeks) may, however, be possible under defined scenarios that specify the future state of critical epidemic drivers, with the additional benefit that such scenarios can be used to anticipate the comparative effect of control measures. Since December 2020, the U.S. COVID-19 Scenario Modeling Hub (SMH) has convened multiple modeling teams to make 6-month ahead projections of the number of SARS-CoV-2 cases, hospitalizations and deaths. The SMH released nearly 1.8 million national and state-level projections between February 2021 and November 2022. SMH performance varied widely as a function of both scenario validity and model calibration. Scenario assumptions were periodically invalidated by the arrival of unanticipated SARS-CoV-2 variants, but SMH still provided projections on average 22 weeks before changes in assumptions (such as virus transmissibility) invalidated scenarios and their corresponding projections. During these periods, before emergence of a novel variant, a linear opinion pool ensemble of contributed models was consistently more reliable than any single model, and projection interval coverage was near target levels for the most plausible scenarios (e.g., 79% coverage for 95% projection interval). SMH projections were used operationally to guide planning and policy at different stages of the pandemic, illustrating the value of the hub approach for long-term scenario projections.


Reference Type

Journal Article

Year Published


Journal Title



Howerton, Emily
Contamin, Lucie
Mullany, Luke C.
Qin, Michelle
Reich, Nicholas G.
Bents, Samantha
Borchering, Rebecca K.
Jung, Sung-Mok
Loo, Sara L.
Smith, Claire P.
Levander, John
Kerr, Jessica
Espino, J.
van Panhuis, Willem G.
Hochheiser, Harry
Galanti, Marta
Yamana, Teresa
Pei, Sen
Shaman, Jeffrey
Rainwater-Lovett, Kaitlin
Kinsey, Matt
Tallaksen, Kate
Wilson, Shelby
Shin, Lauren
Lemaitre, Joseph C.
Kaminsky, Joshua
Hulse, Juan D.
Lee, Elizabeth C.
McKee, Clif
Hill, Alison
Karlen, Dean
Chinazzi, Matteo
Davis, Jessica T.
Mu, Kunpeng
Xiong, Xinyue
Pastore Y Piontti, Ana
Vespignani, Alessandro
Rosenstrom, Erik T.
Ivy, Julie S.
Mayorga, Maria E.
Swann, Julie L.
España, Guido
Cavany, Sean
Moore, Sean M.
Perkins, Alex
Hladish, Thomas
Pillai, Alexander
Toh, Kok B.
Longini, Ira M., Jr.
Chen, Shi
Paul, Rajib
Janies, Daniel
Thill, Jean-Claude
Bouchnita, Anass
Bi, Kaiming
Lachmann, Michael
Fox, Spencer
Meyers, Lauren A.
Srivastava, Ajitesh
Porebski, Przemyslaw
Venkatramanan, Srini
Adiga, Aniruddha
Lewis, Bryan
Klahn, Brian
Outten, Joseph
Hurt, Benjamin
Chen, Jiangzhuo
Mortveit, Henning
Wilson, Amanda
Marathe, Madhav
Hoops, Stefan
Bhattacharya, Parantapa
Machi, Dustin
Cadwell, Betsy L.
Healy, Jessica M.
Slayton, Rachel B.
Johansson, Michael A.
Biggerstaff, Matthew
Truelove, Shaun A.
Runge, Michael C.
Shea, Katriona
Viboud, Cécile
Lessler, Justin

Article Type





United States




Lessler - 0000-0002-9741-8109