Transforming Norm-based To Graph-based Spatial Representation for Spatio-Temporal Epidemiological Models

• URL: http://arxiv.org/abs/2402.14539v3
• Date: Sat, 30 Aug 2025 11:34:34 GMT
• Title: Transforming Norm-based To Graph-based Spatial Representation for Spatio-Temporal Epidemiological Models
• Authors: Teddy Lazebnik,
• Abstract summary: Pemics pose significant threats to global health, mortality, economic stability, and political landscapes.<n>Models can be roughly divided into two main spatial categories: norm-based graph-based models.<n>In this study, we explore the ability to transform from norm-based to graph-based spatial representation.
• Score: 6.396288020763144
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Pandemics, with their profound societal and economic impacts, pose significant threats to global health, mortality rates, economic stability, and political landscapes. In response to these challenges, 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. Norm-based models are usually more accurate and easier to model but are more computationally intensive and require more data to fit. On the other hand, graph-based models are less accurate and harder to model but are less computationally intensive and require fewer data to fit. As such, ideally, one would like to use a graph-based model while preserving the representation accuracy obtained by the norm-based model. In this study, we explore the ability to transform from norm-based to graph-based spatial representation for these models. We first show no analytical mapping between the two exists, requiring one to use approximation numerical methods instead. 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. We investigate our framework for three real-world cases, achieving 94\% accuracy preservation, on average. Moreover, an analysis of synthetic cases shows the proposed framework is relatively robust for changes in both spatial and temporal properties.

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