Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challenges

Yang Ye, Abhishek Pandey, Carolyn Bawden, Dewan Md. Sumsuzzman, Rimpi Rajput, Affan Shoukat, Burton H. Singer, Seyed M. Moghadas and Alison P. Galvani ()
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Yang Ye: Yale School of Public Health
Abhishek Pandey: Yale School of Public Health
Carolyn Bawden: McGill University
Dewan Md. Sumsuzzman: York University
Rimpi Rajput: Yale School of Public Health
Affan Shoukat: University of Regina
Burton H. Singer: University of Florida
Seyed M. Moghadas: York University
Alison P. Galvani: Yale School of Public Health

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Integrating prior epidemiological knowledge embedded within mechanistic models with the data-mining capabilities of artificial intelligence (AI) offers transformative potential for epidemiological modeling. While the fusion of AI and traditional mechanistic approaches is rapidly advancing, efforts remain fragmented. This scoping review provides a comprehensive overview of emerging integrated models applied across the spectrum of infectious diseases. Through systematic search strategies, we identified 245 eligible studies from 15,460 records. Our review highlights the practical value of integrated models, including advances in disease forecasting, model parameterization, and calibration. However, key research gaps remain. These include the need for better incorporation of realistic decision-making considerations, expanded exploration of diverse datasets, and further investigation into biological and socio-behavioral mechanisms. Addressing these gaps will unlock the synergistic potential of AI and mechanistic modeling to enhance understanding of disease dynamics and support more effective public health planning and response.

Date: 2025
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DOI: 10.1038/s41467-024-55461-x

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