An open challenge to advance probabilistic forecasting for dengue epidemics
Authors: Johansson Michael A. , Apfeldorf Karyn M. , Dobson Scott , Devita Jason , Buczak Anna L. , Baugher Benjamin , Moniz Linda J. , Bagley Thomas , Babin Steven M. , Guven Erhan , Yamana Teresa K. , Shaman Jeffrey , Moschou Terry , Lothian Nick , Lane Aaron , Osborne Grant , Jiang Gao , Brooks Logan C. , Farrow David C. , Hyun Sangwon , Tibshirani Ryan J. , Rosenfeld Roni , Lessler Justin , Reich Nicholas G. , Cummings Derek A. T. , Lauer Stephen A. , Moore Sean M. , Clapham Hannah E. , Lowe Rachel , Bailey Trevor C. , García-Díez Markel , Carvalho Marilia Sá , Rodó Xavier , Sardar Tridip , Paul Richard , Ray Evan L. , Sakrejda Krzysztof , Brown Alexandria C. , Meng Xi , Osoba Osonde , Vardavas Raffaele , Manheim David , Moore Melinda , Rao Dhananjai M. , Porco Travis C. , Ackley Sarah , Liu Fengchen , Worden Lee , Convertino Matteo , Liu Yang , Reddy Abraham , Ortiz Eloy , Rivero Jorge , Brito Humberto , Juarrero Alicia , Johnson Leah R. , Gramacy Robert B. , Cohen Jeremy M. , Mordecai Erin A. , Murdock Courtney C. , Rohr Jason R. , Ryan Sadie J. , Stewart-Ibarra Anna M. , Weikel Daniel P. , Jutla Antarpreet , Khan Rakibul , Poultney Marissa , Colwell Rita R. , Rivera-García Brenda , Barker Christopher M. , Bell Jesse E. , Biggerstaff Matthew , Swerdlow David , Mier-y-Teran-Romero Luis , Forshey Brett M. , Trtanj Juli , Asher Jason , Clay Matt , Margolis Harold S. , Hebbeler Andrew M. , George Dylan , Chretien Jean-Paul 
Published: November 2019

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project—integration with public health needs, a common forecasting framework, shared and standardized data, and open participation—can help advance infectious disease forecasting beyond dengue.