Citation
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, et al. (2019). An Open Challenge to Advance Probabilistic Forecasting for Dengue Epidemics. Proceedings of the National Academy of Sciences of the United States of America, 116(48), 24268-24274. PMCID: PMC6883829Abstract
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.URL
http://dx.doi.org/10.1073/pnas.1909865116Reference Type
Journal ArticleYear Published
2019Journal Title
Proceedings of the National Academy of Sciences of the United States of AmericaSeries Title
Proc Natl Acad Sci U S A. 2019 Dec 17;116(51):26087-26088. doi: 10.1073/pnas.1920071116. Epub 2019 Dec 9. PMID: 31818939Proc Natl Acad Sci U S A. 2020 Aug 18;117(33):20336. doi: 10.1073/pnas.2014829117. Epub 2020 Aug 10. PMID: 32778595
Author(s)
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