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Infectious diseases usually originate from a specific location within a city.Due to the heterogenous distribution of population and public facilities, andthe structural heterogeneity of human mobility network embedded in space,infectious diseases break out at different locations would cause differenttransmission risk and control difficulty. This study aims to investigate theimpact of initial outbreak locations on the risk of spatiotemporal transmissionand 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 simulatethe spreading process of an infectious disease (i.e., COVID-19) acrossfine-grained intra-urban regions (i.e., 649 communities of Shenzhen City,China). Based on the simulation model, we evaluated the transmission riskcaused by different initial outbreak locations by proposing three indexesincluding the number of infected cases (CaseNum), the number of affectedregions (RegionNum), and the spatial diffusion range (SpatialRange). Finally,we investigated the contribution of different influential factors to thetransmission risk via machine learning models. Results indicates that differentinitial outbreak locations would cause similar CaseNum but different RegionNumand SpatialRange. To avoid the epidemic spread quickly to more regions, it isnecessary to prevent epidemic breaking out in locations with highpopulation-mobility flow density. While to avoid epidemic spread to largerspatial range, remote regions with long daily trip distance of residents needattention. Those findings can help understand the transmission risk and drivingforce of initial outbreak locations within cities and make precise preventionand control strategies in advance.

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