Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area

• URL: http://arxiv.org/abs/2204.10752v1
• Date: Wed, 23 Mar 2022 08:21:32 GMT
• Title: Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area
• Authors: Kang Liu, Ling Yin, Jianzhang Xue
• Abstract summary: Infectious diseases usually originate from a specific location within a city. Due to the heterogenous distribution of population and public facilities, infectious diseases break out at different locations would cause different transmission risk and control difficulty. This study aims to investigate the impact of initial outbreak locations on the risk of transmission and reveal the driving force behind high outbreak-risk locations.
• Score: 4.738791349182193
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infectious diseases usually originate from a specific location within a city. Due to the heterogenous distribution of population and public facilities, and the structural heterogeneity of human mobility network embedded in space, infectious diseases break out at different locations would cause different transmission risk and control difficulty. This study aims to investigate the impact of initial outbreak locations on the risk of spatiotemporal transmission and reveal the driving force behind high-risk outbreak locations. First, integrating mobile phone location data, we built a SLIR (susceptible-latent-infectious-removed)-based meta-population model to simulate the spreading process of an infectious disease (i.e., COVID-19) across fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City, China). Based on the simulation model, we evaluated the transmission risk caused by different initial outbreak locations by proposing three indexes including the number of infected cases (CaseNum), the number of affected regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally, we investigated the contribution of different influential factors to the transmission risk via machine learning models. Results indicates that different initial outbreak locations would cause similar CaseNum but different RegionNum and SpatialRange. To avoid the epidemic spread quickly to more regions, it is necessary to prevent epidemic breaking out in locations with high population-mobility flow density. While to avoid epidemic spread to larger spatial range, remote regions with long daily trip distance of residents need attention. Those findings can help understand the transmission risk and driving force of initial outbreak locations within cities and make precise prevention and control strategies in advance.

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