Advancing adoptability and sustainability of digital prediction tools for climate-sensitive infectious disease prevention and control

Dung Phung (), Felipe J. Colón-González, Daniel M. Weinberger, Vinh Bui, Son Nghiem, Cordia Chu, Hai Phung, Nam Sinh Vu, Quang- Van Doan, Masahiro Hashizume, Colleen L. Lau, Simon Reid, Lan Trong Phan, Duong Nhu Tran, Cong Tuan Pham, Kien Quoc Do and Robert Dubrow ()
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Dung Phung: The University of Queensland
Felipe J. Colón-González: Data for Science and Health, Wellcome Trust
Daniel M. Weinberger: Yale University
Vinh Bui: Southern Cross University
Son Nghiem: Australian National University
Cordia Chu: Griffith University
Hai Phung: Griffith University
Nam Sinh Vu: National Institute of Hygiene and Epidemiology
Quang- Van Doan: University of Tsukuba
Masahiro Hashizume: The University of Tokyo
Colleen L. Lau: The University of Queensland
Simon Reid: The University of Queensland
Lan Trong Phan: National Institute of Hygiene and Epidemiology
Duong Nhu Tran: National Institute of Hygiene and Epidemiology
Cong Tuan Pham: Griffith University
Kien Quoc Do: The University of Queensland
Robert Dubrow: Yale University

Nature Communications, 2025, vol. 16, issue 1, 1-8

Abstract: Abstract Few forecasting models have been translated into digital prediction tools for prevention and control of climate-sensitive infectious diseases. We propose a 3-U (useful, usable, and used) research framework for advancing the adoptability and sustainability of these tools. We make recommendations for 1) developing a tool with a high level of accuracy and sufficient lead time to permit effective proactive interventions (useful); 2) conducting a needs assessment to ensure that a tool meets the needs of end-users (usable); and 3) demonstrating the efficacy and cost-effectiveness of a tool to secure its adoption into routine surveillance and response systems (used).

Date: 2025
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DOI: 10.1038/s41467-025-56826-6

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