  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.
