2026-01-23 ウースター工科大学(WPI)
<関連情報>
- https://www.wpi.edu/news/announcements/wpi-led-study-finds-ai-model-can-predict-and-help-contain-disease-outbreaks-confined-spaces
- https://www.pnas.org/doi/10.1073/pnas.2422574123
宿主-病原体エージェントベースシステムを用いた閉鎖環境における感染症発生のリアルタイム時空間追跡 Real-time spatiotemporal tracking of infectious outbreaks in confined environments with a host–pathogen agent-based system
Suhas Srinivasan, Jeffrey King, Jacob M. Collins, +1 , and Dmitry Korkin
Proceedings of the National Academy of Sciences Published:January 20, 2026
DOI:https://doi.org/10.1073/pnas.2422574123
Significance
Infection outbreaks in confined environments, such as schools, nursing homes, college dormitories, or cruise ships, can pose substantial medical risks to large populations within short timeframes. Modeling such outbreaks is challenging due to diverse behaviors of host populations and variations in transmission dynamics across different pathogens. Here, we introduce an approach that integrates state-of-the-art AI and geographic information system technologies. Hosts are represented as thousands of agents, intelligent programs running in parallel and sharing information about the infection, while pathogens are specified through mathematical functions capturing their biological properties. We apply our approach to study outbreaks of key human infections on cruise ships, showing its high accuracy on historical data and demonstrating the efficacy of selected protocols to contain infections.
Abstract
Deadly infection outbreaks in confined spaces, whether it is a COVID-19 outbreak on a cruise ship or measles and stomach flu outbreaks in schools, can be characterized by their rapid spread due to the abundance of common spaces, shared airways, and high population density. Preventing future outbreaks and developing efficient mitigation protocols can benefit from advanced computational modeling approaches. Here, we developed an agent-based modeling approach to study the spatiotemporal dynamics of an infection outbreak in a confined environment caused by a specific pathogen, and to determine effective containment protocols. The approach integrates the 3D geographic information system of a confined environment, behavior of the hosts, key biological parameters about the pathogen obtained from the experimental data, and the general mechanics of host–pathogen and pathogen–fomite interactions. To assess our approach, we applied it to the historical data of infectious outbreaks caused by norovirus, H1N1 influenza A, and SARS-CoV-2 viruses. Our AI-GIS Infection Dynamics (AGID) model accurately predicted daily infection numbers, correctly identified the day when the CDC vessel sanitation protocol would be triggered, singled out key biological parameters affecting the infection spread, and propose pathogen-specific changes to existing containment protocols. Our work advances the understanding of infection spread on cruise ships while offering insights applicable to other similar confined settings, such as nursing homes, schools, and hospitals. By providing a robust framework for real-time outbreak modeling, this study proposes more effective containment protocols and enhances our preparedness for managing infectious diseases and emerging pathogens in confined environments.
