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

ABSTRACT

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 (CO‐ VID ‐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 compa‐ rable 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 ap‐ proach 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 perfor‐ mance of population flow data to spatial distribution of active COVID‐19 cases. (English) [ABSTRACT FROM AUTHOR] 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，其对流动人群实际带来的疫情传播风险分布表征有一定的局限 性。因此，有必要引入电子地图路径规划、列车班次数据等详尽的互联网交通数据，将人群的实际路径纳入对 区域疫情分布的考量中。基于人群流动和交通信息，提出了使用时序分析和路径推断的区域疫情风险扩散分 析和交通管制支持框架，以期提高应对传染病疫情的区域空间治理能力。在历史人群流动与现有病例分布的 基础上，引入公路、铁路路径参数来推断流动人群对途经地区疫情的影响，以疫情初期的病例分布、人群流动 和交通情况为例，对该方法进行了分析验证。结果表明，引入路径途经点参数能够明显提高利用人群流动数 据拟合疫情空间分布的准确性。 (Chinese) [ABSTRACT FROM AUTHOR] Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)