ArticleFrontiers in big data2022
Research on Spatial-temporal Spread and Risk Profile of the COVID-19 Epidemic Based on Mobile Phone Trajectory Data.
Article in Frontiers in big data, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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7 authors.
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Abstract
The COVID-19 epidemic poses a significant challenge to the operation of society and the resumption of work and production. How to quickly track the resident location and activity trajectory of the population, and identify the spread risk of the COVID-19 in geospatial space has important theoretical and practical significance for controlling the spread of the virus on a large scale. In this study, we take the geographical community as the research object, and use the mobile phone trajectory data to construct the spatiotemporal profile of the potential high-risk population. First, by using the spatiotemporal data collision method, identify, and recover the trajectories of the people who were in the same area with the confirmed patients during the same time. Then, based on the range of activities of both cohorts (the confirmed cases and the potentially infected groups), we analyze the risk level of the relevant places and evaluate the scale of potential spread. Finally, we calculate the probability of infection for different communities and construct the spatiotemporal profile for the transmission to help guide the distribution of preventive materials and human resources. The proposed method is verified using survey data of 10 confirmed cases and statistical data of 96 high-risk neighborhoods in Chengdu, China, between 15 January 2020 and 15 February 2020. The analysis finds that the method accurately simulates the spatiotemporal spread of the epidemic in Chengdu and measures the risk level in specific areas, which provides an objective basis for the government and relevant parties to plan and manage the prevention and control of the epidemic.
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