Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 116, Issue 48, Pages 24268-24274Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1909865116
Keywords
forecast; dengue; epidemic; Peru; Puerto Rico
Categories
Funding
- National Institute of General Medical Sciences (NIGMS) [GM110748]
- Defense Threat Reduction Agency [HDTRA1-15-C-0018]
- NIGMS Award [U54 GM088491]
- NSF Graduate Research Fellowship Program [DGE-1252522]
- NIH T32 Training Grant [T32 EB009403]
- HHMI-National Institute of Biomedical Imaging and Bioengineering Interfaces Initiative
- National Institute of Allergy and Infectious Diseases (NIAID) [AI102939]
- NIAID
- NIGMS [R21AI115173, R01AI102939, R35GM119582, U01-GM087728]
- Royal Society Dorothy Hodgkin Fellowship
- Forces and Resources Policy Center of the RAND National Defense Research Institute with discretionary US Department of Defense funds
- Global Institute for Collaborative Research and Education Big-Data and Cybersecurity Station
- Faculty in Industry Award at the University of Minnesota Informatics Institute
- NIH-NSF-US Department of Agriculture Ecology of Infectious Diseases [1R01AI122284]
- Office of the Assistant Secretary for Preparedness and Response, Biomedical Advanced Research and Development Authority [HHSO100201600017I]
- NSF [DMS-1309174]
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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.
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