Pandemics, with their profound societal and economic impacts, pose significant threats to global health, mortality rates, economic stability, and political landscapes. In response to the persistent challenges posed by emerging and reemerging pandemics, numerous studies have employed spatio-temporal models to enhance our understanding and management of these complex phenomena. These spatio-temporal models can be roughly divided into two main spatial categories: norm-based and graph-based trade-offering between accuracy, computational burden, and representational feasibility. In this study, we explore the ability to transform from norm-based to graph-based spatial representation for these models. We introduce a novel framework for this task together with twelve possible implementations using a wide range of heuristic optimization approaches. Our findings show that by leveraging agent-based simulations and heuristic algorithms for the graph node's location and population's spatial walk dynamics approximation one can use graph-based spatial representation without losing much of the model's accuracy and expressiveness. For three real-world cases, the best-performing algorithmic configuration archives 94\% accuracy presence, on average. Moreover, an analysis of synthetic cases shows the proposed framework is relatively robust, as fluctuation in both spatial and temporal dynamics is not badly reflected by the framework's performance.
翻译:暂无翻译