An open challenge to advance probabilistic forecasting for dengue epidemics

Details

• Alternative Title:
  Proc Natl Acad Sci U S A
• Personal Author:
  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
• Description:
  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.
• Subjects:
  Biological Sciences Dengue Disease Outbreaks Epidemics Epidemiologic Methods Humans Incidence Models, Statistical
• Source:
  Proc Natl Acad Sci U S A. 2019; 116(48):24268-24274
• Pubmed ID:
  31712420
• Pubmed Central ID:
  PMC6883829
• Document Type:
  Journal Article
• Funding:
  U01 GM087728/GM/NIGMS NIH HHS/United States ; T32 EB009403/EB/NIBIB NIH HHS/United States ; R35 GM119582/GM/NIGMS NIH HHS/United States ; U01 GM110748/GM/NIGMS NIH HHS/United States ; R01 AI102939/AI/NIAID NIH HHS/United States ; R21 AI115173/AI/NIAID NIH HHS/United States ; U54 GM088491/GM/NIGMS NIH HHS/United States ; R01 AI122284/AI/NIAID NIH HHS/United States ; HHMI/Howard Hughes Medical Institute/United States
• Place as Subject:
  Peru ; Puerto Rico
• Volume:
  116
• Issue:
  48
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:085cd505d858e042a2ea8b8a62cbcb8ed07c42421cc2c47693a05299c97347df
• Download URL:
  https://stacks.cdc.gov/view/cdc/89379/cdc_89379_DS1.pdf
• File Type:
  [PDF - 1.01 MB ]