论文标题
初步爆发地点对城市内部地区传染病风险的影响
Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area
论文作者
论文摘要
传染病通常起源于城市内的特定位置。由于人口和公共设施的异质分布以及嵌入太空中的人类流动网络的结构异质性，在不同位置爆发的传染病会导致不同的传播风险和控制困难。这项研究旨在研究初始爆发地点对时空传播风险的影响，并揭示高风险爆发地点背后的驱动力。首先，我们整合了手机位置数据，我们建立了一个基于基于的元元群体群体模型的SLIR（易感叶子 - 感染型），以模拟跨越细绿色的城市内部地区的传染性疾病（即Covid-19）的扩散过程（即，在中国的山地城649个社区）。基于模拟模型，我们通过提出三个索引，包括感染病例的数量（Casenum），受影响区域（regionNum）和空间扩散范围（空间扩散范围），评估了不同初始爆发地点引起的传播风险。最后，我们通过机器学习模型研究了不同影响因素对传输风险的贡献。结果表明，不同的初始爆发位置会导致类似的casenum，但区域性和空间量不同。为了避免流行病迅速扩散到更多地区，有必要防止流行流动性流量密度较高的位置爆发。为了避免流行病扩散到较大的空间范围，居民每日旅行距离很长的远程区域需要注意。这些发现可以帮助了解城市内初次爆发地点的传播风险和驱动力，并提前进行精确的预防和控制策略。
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.
