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<p>Understanding the impact of population mobility on the spread of infectious diseases is crucial for designing effective interventions. Traditional models, such as origin-destination matrices, often lack the spatial and temporal resolution needed to accurately capture these dynamics. This study addresses this gap by introducing a novel methodology to estimate time-varying occupancy patterns across urban zones (patches) using geospatial data from mobile phones. By leveraging Brownian bridge models at an inhabitant-patch level, we construct a residence-occupation matrix (ROM) that represents the fraction of time individuals spend in each urban patch. We apply this approach to real-world data from Hermosillo, Sonora, Mexico, during the COVID-19 pandemic. Our findings show that even small shifts in local mobility patterns can significantly alter the epidemic’s trajectory, highlighting the importance of high-resolution mobility data in modeling infectious disease spread. These changes can be patch-specific, and their contribution to the overall evolution depends on the mobility dynamics and population sizes within each patch. The proposed ROM serves as a key input for multi-patch epidemiological models, that in turn, can provide more realistic epidemic forecasts, facilitating the evaluation of the effectiveness of patch-specific and global mobility restrictions, and improving the estimation of epidemiological parameters of such models in related research. Additionally, the ROM framework can be adapted to other patch-based models modeling various phenomena influenced by human mobility.</p>

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Use of mobile phone sensing data to estimate residence and occupation times in urban patches: human mobility restrictions and the 2020 COVID-19 outbreak in Hermosillo, Mexico

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月25日

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  • L. Leticia Ramírez-Ramírez,
  • José A. Montoya,
  • Jesús F. Espinoza,
  • Chahak Mehta,
  • Albert Orwa Akuno,
  • Tan Bui–Thanh

摘要

Understanding the impact of population mobility on the spread of infectious diseases is crucial for designing effective interventions. Traditional models, such as origin-destination matrices, often lack the spatial and temporal resolution needed to accurately capture these dynamics. This study addresses this gap by introducing a novel methodology to estimate time-varying occupancy patterns across urban zones (patches) using geospatial data from mobile phones. By leveraging Brownian bridge models at an inhabitant-patch level, we construct a residence-occupation matrix (ROM) that represents the fraction of time individuals spend in each urban patch. We apply this approach to real-world data from Hermosillo, Sonora, Mexico, during the COVID-19 pandemic. Our findings show that even small shifts in local mobility patterns can significantly alter the epidemic’s trajectory, highlighting the importance of high-resolution mobility data in modeling infectious disease spread. These changes can be patch-specific, and their contribution to the overall evolution depends on the mobility dynamics and population sizes within each patch. The proposed ROM serves as a key input for multi-patch epidemiological models, that in turn, can provide more realistic epidemic forecasts, facilitating the evaluation of the effectiveness of patch-specific and global mobility restrictions, and improving the estimation of epidemiological parameters of such models in related research. Additionally, the ROM framework can be adapted to other patch-based models modeling various phenomena influenced by human mobility.