利用区域人群流动和新兴交通数据支持疫情防控

在传染病疫情早期,对出现疫情的地区进行及时管控、防止疫情跨区域传播,对于减少感染量、减轻疫区应对和救治压力、保障疫情期间社会经济平稳具有重要意义.防止疫情跨区域传播的前提是掌握现有病例在区域中的当前空间分布和预期空间分布.目前常用的人群流动数据仅能提供人群的长期驻留地点,而不能提供短期驻留地或者乘坐的交通工具信息,其对...

Supporting Epidemic Control with Regional Population Flow Data and Nova Transportation Data

It is imperative to prevent interregional transmission in the early stages of an epidemic for both controlling the epidemic and ensuring socioeconomic stability. The premises of such exercise are knowing the present and upcoming spatial distribution of any existing cases. During the coronavirus disease 2019(COVID-19) epidemic, researchers have used location-based services data to extract the origins and destinations of travelers and thus analyze the spatial distribution of the epidemic. However, these data can only provide positions of long-term stays of travelers, but not short-term stops and the vehicles they are taking, which are also common spaces of transmission. Hence it is necessary to introduce online transportation data such as route recommendation and train tables to characterize the route taken by interregional travelers when evaluating the distribution of existing cases. We propose an approach to support risk evaluation of regional epidemic spread and regional transportation control, aiming to improve our spatial governing capabilities in face of an epidemic. It involves estimating outflow cases using recent population flows and previous comparable flows, projecting the probable route they will take using online map route recommendation and flight calendar/train tables, locating short-term stops according to the projected routes, and thus formulating transportation restriction policies to lower further regional transmission. The key and distinct step of this approach is to locate potential stops of regional travelers, which is achieved by combining the proportion of transportation mode choice and minimum time strategy. The effectiveness and necessity of introducing probable routes are verified with active cases data, population flow data and transportation data in January,2020. Results show that introducing anticipated short-term stops significantly improves the fitting performance of population flow data to spatial distribution of active COVID-19 cases.