TY - JOUR
T1 - Trade-offs between individual and ensemble forecasts of an emerging infectious disease
AU - Oidtman, Rachel J.
AU - Omodei, Elisa
AU - Kraemer, Moritz U.G.
AU - Castañeda-Orjuela, Carlos A.
AU - Cruz-Rivera, Erica
AU - Misnaza-Castrillón, Sandra
AU - Cifuentes, Myriam Patricia
AU - Rincon, Luz Emilse
AU - Cañon, Viviana
AU - Alarcon, Pedro de
AU - España, Guido
AU - Huber, John H.
AU - Hill, Sarah C.
AU - Barker, Christopher M.
AU - Johansson, Michael A.
AU - Manore, Carrie A.
AU - Reiner,, Robert C.
AU - Rodriguez-Barraquer, Isabel
AU - Siraj, Amir S.
AU - Frias-Martinez, Enrique
AU - García-Herranz, Manuel
AU - Perkins, T. Alex
N1 - Funding Information:
The authors would like to thank Clara Palau Montava for help with managing the early stages of this project and Chris Fabian, Evan Wheeler, and Vedran Sekara for comments, suggestions, and support throughout the duration of this project. The authors would like to thank Sebastian Baña for technical support in running models on the Databricks computing platform. The authors would additionally like to thank the UNICEF-Colombia Representative, Aida Oliver Arostegui, INS Director, Martha Lucia Ospina Martinez, and the past and present Ministers of the Colombia Ministry of Health, Juan Pablo Uribe Restrepo and Fernado Ruiz Gomez. Lastly, the authors would like to thank two anonymous reviewers for their constructive comments and useful suggestions. R.J.O. acknowledges support from an Eck Institute for Global Health Fellowship, GLOBES grant, Arthur J. Schmitt Fellowship, and the UNICEF Office of Innovation. M.U.G.K. is supported by The Branco Weiss Fellowship—Society in Science, administered by the ETH Zurich and acknowledges funding from the Oxford Martin School and the European Union Horizon 2020 project MOOD (#874850). J.H.H. acknowledges funding from a National Science Foundation Graduate Research Fellowship and a Richard and Peggy Notebaert Premier Fellowship. S.C.H. is supported by the Wellcome Trust (220414/Z/20/Z). This research was funded in whole, or in part, by the Wellcome Trust [Grant no. 220414/Z/20/Z]. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted paper version arising from this submission. CMB, MAJ, CAM, RCR Jr., IR-B, ASS, and TAP were supported by a RAPID grant from the National Science Foundation (DEB 1641130).
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-021-25695-0
DO - 10.1038/s41467-021-25695-0
M3 - Article
C2 - 34508077
AN - SCOPUS:85114863038
VL - 12
JO - Nature Communications
JF - Nature Communications
SN - 2041-1723
IS - 1
M1 - 5379
ER -