Details

Autor(en) / Beteiligte

• Oidtman, Rachel J
• Omodei, Elisa
• Kraemer, Moritz U G
• Castañeda-Orjuela, Carlos A
• Cruz-Rivera, Erica
• Misnaza-Castrillón, Sandra
• Cifuentes, Myriam Patricia
• Rincon, Luz Emilse
• Cañon, Viviana
• Alarcon, Pedro de
• España, Guido
• Huber, John H
• Hill, Sarah C
• Barker, Christopher M
• Johansson, Michael A
• Manore, Carrie A
• Reiner, Jr, Robert C
• Rodriguez-Barraquer, Isabel
• Siraj, Amir S
• Frias-Martinez, Enrique
• García-Herranz, Manuel
• Perkins, T Alex

Titel

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

Ist Teil von

• Nature communications, 2021-09, Vol.12 (1), p.5379-11, Article 5379

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2021

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.