Artificial Intelligence for Hantavirus Outbreak Risk Prediction for Public Health Systems

A Multisource Early Warning Framework for Tourism, Hospitality, and Public Health Systems

Abstract

Hantavirus infections remain relatively rare compared with many respiratory epidemics, yet their severe clinical outcomes, rodent-linked environmental exposure pathways, and occasional travel-associated clusters create a complex risk problem for tourism, hospitality, and public health systems. This article addresses the first research objective of a broader AI-driven business continuity study: to examine how artificial intelligence can be used to predict hantavirus outbreak risks. Using a design-science and conceptual modeling approach, the paper proposes the Hantavirus Risk Prediction Artificial Intelligence (HRP-AI) framework, a multisource early warning model that integrates rodent and environmental signals, syndromic surveillance, laboratory data, travel and mobility indicators, digital weak signals, and business vulnerability indicators. The framework applies anomaly detection, spatiotemporal forecasting, natural language processing, ensemble learning, and explainable AI to produce dynamic risk scores for hotels, cruise operators, airlines, restaurants, event centers, tourism agencies, and public health authorities. The article contributes a publishable AI risk architecture, a feature taxonomy, model selection logic, validation metrics, and governance controls for privacy, data quality, and human oversight. The proposed model does not replace public health decision-makers; instead, it supports earlier detection, risk stratification, targeted prevention, transparent communication, and operational preparedness in high-contact travel and hospitality environments. The paper concludes that AI-based hantavirus risk prediction is most credible when it combines epidemiological evidence with environmental exposure data, operational business indicators, and explainable human-in-the-loop decision processes.