Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Rachel J. Oidtman (), Elisa Omodei, Moritz U. G. Kraemer, Carlos A. Castañeda-Orjuela, Erica Cruz-Rivera, Sandra Misnaza-Castrillón, Myriam Patricia Cifuentes, Luz Emilse Rincon, Viviana Cañon, Pedro de Alarcon, Guido España, John H. Huber, Sarah C. Hill, Christopher M. Barker, Michael A. Johansson, Carrie A. Manore, Robert C. Reiner, Jr., Isabel Rodriguez-Barraquer, Amir S. Siraj, Enrique Frias-Martinez, Manuel García-Herranz () and T. Alex Perkins ()
Additional contact information
Rachel J. Oidtman: University of Notre Dame
Elisa Omodei: UNICEF
Moritz U. G. Kraemer: University of Oxford
Carlos A. Castañeda-Orjuela: Instituto Nacional de Salud
Erica Cruz-Rivera: Instituto Nacional de Salud
Sandra Misnaza-Castrillón: Instituto Nacional de Salud
Myriam Patricia Cifuentes: Ministerio de Salud y Protección Social
Luz Emilse Rincon: Ministerio de Salud y Protección Social
Viviana Cañon: UNICEF
Pedro de Alarcon: LUCA Telefonica Data Unit
Guido España: University of Notre Dame
John H. Huber: University of Notre Dame
Sarah C. Hill: University of Oxford
Christopher M. Barker: University of California
Michael A. Johansson: Centers for Disease Control and Prevention
Carrie A. Manore: Los Alamos National Laboratory
Robert C. Reiner, Jr.: University of Washington
Isabel Rodriguez-Barraquer: University of California
Amir S. Siraj: University of Notre Dame
Enrique Frias-Martinez: Telefonica Research
Manuel García-Herranz: UNICEF
T. Alex Perkins: University of Notre Dame

Nature Communications, 2021, vol. 12, issue 1, 1-11

Abstract: Abstract Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25695-0

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DOI: 10.1038/s41467-021-25695-0

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