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 - Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12/1
Y1 - 2021/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=85114863038&partnerID=8YFLogxK
U2 - 10.1038/s41467-021-25695-0
DO - 10.1038/s41467-021-25695-0
M3 - Article
C2 - 34508077
AN - SCOPUS:85114863038
SN - 2041-1723
VL - 12
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 5379
ER -