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This study introduces an intermediate-level modeling approach that bridges the gap between complex agent-based modelsAgent Based Modeling (ABM) (ABMs) and traditional compartmental models for infectious diseases. This novel, geospatial information-aware ABM, termed agent-in-cell (AIC), leverages key concepts such as high-risk regions and points-of-interest (POIs). Its main contribution lies in the development of a robust and flexible methodology for constructing practicable ABMs, enhancing disease modeling in urban environments, and supporting public health strategies that require geographic specificity. AIC is designed to integrate a diverse set of data, including high-resolution mobilityMobility data from SafeGraph and coarser medical datasets. We demonstrate that AIC successfully replicates historical trends observed during the COVID-19COVID-19 pandemic and provides reliable short-term spatiotemporal predictions of how pandemicsPandemics may evolve from their current state. This chapter is complemented by Chap. 6 in this book by Sikaroudi, Efrat, and Chertkov, Agent-In-Cell Modeling of a Pandemic: Harnessing Super-Agents for Predictive Modeling.

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Agent-in-Cell Modeling of Pandemics

  • 章节

  • 发表日期：

    2026年

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  • Amir Mohammad Esmaieeli Sikaroudi,
  • Alon Efrat,
  • Michael Chertkov

摘要

This study introduces an intermediate-level modeling approach that bridges the gap between complex agent-based modelsAgent Based Modeling (ABM) (ABMs) and traditional compartmental models for infectious diseases. This novel, geospatial information-aware ABM, termed agent-in-cell (AIC), leverages key concepts such as high-risk regions and points-of-interest (POIs). Its main contribution lies in the development of a robust and flexible methodology for constructing practicable ABMs, enhancing disease modeling in urban environments, and supporting public health strategies that require geographic specificity. AIC is designed to integrate a diverse set of data, including high-resolution mobilityMobility data from SafeGraph and coarser medical datasets. We demonstrate that AIC successfully replicates historical trends observed during the COVID-19COVID-19 pandemic and provides reliable short-term spatiotemporal predictions of how pandemicsPandemics may evolve from their current state. This chapter is complemented by Chap. 6 in this book by Sikaroudi, Efrat, and Chertkov, Agent-In-Cell Modeling of a Pandemic: Harnessing Super-Agents for Predictive Modeling.