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Infectious disease spread within the human population can be conceptualized as a complex system composed of individuals who interact and transmit viruses through spatio-temporal processes that manifest across and between scales. The complexity of this system ultimately means that the spread of infectious diseases is difficult to understand, predict, and respond to effectively. Research interest in GeoAI for public health has been fueled by the increased availability of rich data sources such as human mobility data, OpenStreetMap data, contact tracing data, symptomatic online surveys, retail and commerce data, genomics data, and more. This data availability has resulted in a wide variety of data-driven solutions for infectious disease spread prediction which show potential in enhancing our forecasting capabilities. This book chapter (1) motivates the need for AI-based solutions in public health by showing the heterogeneity of human behavior related to health, (2) provides a brief survey of current state-of-the-art solutions using AI for infectious disease spread prediction, (3) describes a use-case of using large-scale human mobility data to inform AI models for the prediction of infectious disease spread in a city, and (4) provides future research directions and ideas.
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This content will become publicly available on January 16, 2026
Fostering public health and academic partnerships during and beyond a public health emergency: lessons learned from COVID-19
- Award ID(s):
- 1911853
- PAR ID:
- 10610249
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- American Journal of Epidemiology
- Volume:
- 194
- Issue:
- 6
- ISSN:
- 0002-9262
- Page Range / eLocation ID:
- 1482 to 1484
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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