AI-Based Urban Disease Spread Risk Assessment Using Environmental Data Accuracy

Increased urbanization, climate change, and demographic growth caused infectious diseases to proliferate at an ever accelerating rate in urban areas, and while laudably addressing epidemic outbreaks, these diseases cause severe stress to implementation of public health systems. Epidemological analysis that features environmental, demographic and spatial representations of disease processes may not be delineated using the classical epidemiologic methods. This paper suggests to develop a framework for a knowledge-based Artificial Intelligence-based urban disease spread risk determination guided by environmental and demographic data. The framework integrates the application of machine learning models with geospatial analysis in order to determine the major ecological parameters such as air quality, temperature, rainfall, and population density that influence the spread of certain diseases. Based on prediction analytic and visualization tools of Geographic Information System (GIS), the study develops an interactive risk map that can be applied for real-time identification of high-risk areas. This decision support structure enhances the efficacy of proactive decision-making for public health organizations and offers strategic prioritization of resources and the opportunity for opportunistic interventions to enhance the resilience to climate sensitive disease threats. The findings contribute to the call for sophisticated urban health monitoring systems, by providing a model that can be adapted to different urban settings (Jain et al, 2018).