Ensemble approaches for short-term dengue fever forecasts: A global evaluation study

• 成果类型: Article
• 署名作者: Io, Skyler Wu; Meyer, Austin G.; Clemente, Leonardo; Stolerman, Lucas M.; Lu, Fred; Majumder, Atreyee; Verbeeck, Rudi; Masyn, Serge; Santillana, Mauricio
• 署名单位: Harvard University; Northeastern University; Baylor College of Medicine; Oklahoma State University System; Oklahoma State University - Stillwater; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11224
• DOI: 10.1073/pnas.2422335122
• 发表日期: 2025-08-19
• 关键词: model

摘要

Dengue fever, a tropical vector-borne disease, is a leading cause of hospitalization and death in many parts of the world, especially in Asia and Latin America. Where timely dengue surveillance exists, decision-makers can better implement public health measures and allocate resources. Reliable near-term forecasts may help anticipate healthcare demands and promote preparedness. We propose ensemble modeling approaches combining mechanistic, statistical, and machine learning models to forecast dengue cases 1 to 3 mo ahead at the province level across multiple countries. We assess these models' predictive ability out-of-sample and retrospectively in over 180 locations worldwide, including provinces in Brazil, Colombia, Malaysia, Mexico, Thailand, plus Iquitos, Peru, and San Juan, Puerto Rico, during at least 2 to 3 y. We also evaluate ensemble approaches in a real-time, prospective dengue forecasting platform during 2022-2023, considering data availability limitations. Our ensemble modeling leads to an improvement to previous efforts that may help decision-making in the context of large uncertainties. This contrasts with the variable performance of individual component models across locations and time. No single model achieves optimal predictions across all scenarios, but while ensemble models may not always perform best in specific locations, they consistently rank among the top 3 performing models both retrospectively and prospectively.