论文标题
基于活动的接触网络缩放和大都市地区的流行病传播
Activity-based contact network scaling and epidemic propagation in metropolitan areas
论文作者
论文摘要
鉴于城市化和新兴大流行威胁的增长，需要更复杂的模型来了解疾病的传播并研究各种城市类型的干预策略的影响。我们介绍了一个完全机械的，基于活动的和高度时空分辨的流行病学模型，该模型利用了从全面城市中综合的移动性需求和供应模型获得的人对象。在两个具有代表性合成人群和活动性模式的全尺度城市中模拟Covid-19的演变，我们分析了基于活动的接触网络。我们观察到，在两个城市中，过境触点是不含规模的，工程接触是微电分配的，购物或休闲联系人的分布成倍分布。我们还研究了过境网络的影响，发现其去除会损害疾病的传播，而工作对于峰值疾病的扩散也至关重要。我们的框架对现有案例和死亡率数据进行了验证，证明了跟踪和追踪的潜力，以及详细的流行病控制策略的社会人口统计学和流动性分析。
Given the growth of urbanization and emerging pandemic threats, more sophisticated models are required to understand disease propagation and investigate the impacts of intervention strategies across various city types. We introduce a fully mechanistic, activity-based and highly spatio-temporally resolved epidemiological model which leverages on person-trajectories obtained from integrated mobility demand and supply models in full-scale cities. Simulating COVID-19 evolution in two full-scale cities with representative synthetic populations and mobility patterns, we analyze activity-based contact networks. We observe that transit contacts are scale-free in both cities, work contacts are Weibull distributed, and shopping or leisure contacts are exponentially distributed. We also investigate the impact of the transit network, finding that its removal dampens disease propagation, while work is also critical to post-peak disease spreading. Our framework, validated against existing case and mortality data, demonstrates the potential for tracking and tracing, along with detailed socio-demographic and mobility analyses of epidemic control strategies.
