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

TL;DR

This study models how initial outbreak locations within a city influence the spread and control difficulty of infectious diseases like COVID-19, highlighting the importance of targeted prevention based on mobility and location factors.

Contribution

It introduces a fine-grained intra-urban transmission model combining mobile data and machine learning to evaluate outbreak risks based on location.

Findings

Different outbreak locations lead to similar total cases but vary in affected regions and spread range.

High population-mobility areas require focused prevention to prevent rapid epidemic spread.

Remote regions with long daily trips are at risk of wider spatial transmission.

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.