ARTIFICIAL INTELLIGENCE AS A TOOL FOR EARLY WARNING AND MITIGATION OF BIOLOGICAL RISKS IN URBANIZED AREAS

This article addresses the pressing issue of enhancing the resilience of large metropolitan areas to biological threats, using Saint Petersburg and the Leningrad Region as a case study. The authors identify a key institutional and managerial vulnerability in the region: the administrative fragmentation of the healthcare and biosecurity systems between the two constituent entities of the Russian Federation, a problem exacerbated by intensive cross-border commuting. As a solution, a preventive strategy is proposed, centered on an artificial intelligence (AI) – based forecasting mechanism. Employing comparative and economic (cost-benefit) analysis, along with scenario modeling, the study evaluates three response strategies to a hypothetical biological threat. The results demonstrate that a preventive scenario utilizing AI for early threat detection can reduce resource mobilization time from 36–48 hours to 6–10 hours. Despite higher initial costs, this approach leads to an 8 to 9-fold reduction in the projected number of infections and decreases the total economic damage by 4 to 5 times compared to a reactive model. The findings provide a basis for assessing the economic feasibility of transitioning to proactive protection systems for megacities.