Decision Support Component for the Localized Epidemiological Modelling of COVID-19

This study develops a decision support system for localized epidemiological modelling of infectious disease spread. We propose a Bayesian network topology for performing inference supplementary to an epidemiological simulation framework and a cohesive integration of this decision support system with the framework. The Bayesian network topology is structured with data defined as inputs, outputs, or derived features within the simulation framework. All features are mo-tivated by their clinical relevance and utility for administrative policy guidance. Edges in the final network are quantitatively assessed using structural equation modelling to ensure strong causal connections. Various inference scenarios are demon-strated to provide proof of concept for real-world application and validation in future directions. The outcomes of this project contribute to a larger body of work for infectious disease risk mitigation and emergency management in generalized environments.