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,, Isabel Rodriguez-Barraquer, Amir S. Siraj, Enrique Frias-MartinezManuel García-Herranz, T. Alex Perkins

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number5379
JournalNature communications
Volume12
Issue number1
DOIs
StatePublished - Dec 2021
Externally publishedYes

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Physics and Astronomy(all)

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