Machine learning based regional epidemic transmission risks precaution in digital society.

Autor: Shi Z; School of Data Science, Fudan University, Shanghai, 200433, China., Qian H; Institute for Global Public Policy, Fudan University, Shanghai, 200433, China. qianhaoqi@fudan.edu.cn.; LSE-Fudan Research Centre for Global Public Policy, Fudan University, Shanghai, 200433, China. qianhaoqi@fudan.edu.cn.; MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, 200433, China. qianhaoqi@fudan.edu.cn., Li Y; Shanghai Ideal Information Industry (Group) Co., Ltd, Fudan University, Shanghai, 200120, China., Wu F; Shanghai Public Health Clinical Center, Fudan University, Shanghai, 200032, China.; Key Laboratory of Medical Molecular Virology, Fudan University, Shanghai, 200032, China., Wu L; MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, 200433, China. wulibo@fudan.edu.cn.; School of Economics, Fudan University, Shanghai, 200433, China. wulibo@fudan.edu.cn.; Institute for Big Data, Fudan University, Shanghai, 200433, China. wulibo@fudan.edu.cn.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2022 Nov 28; Vol. 12 (1), pp. 20499. Date of Electronic Publication: 2022 Nov 28.
DOI: 10.1038/s41598-022-24670-z
Abstrakt: The contact and interaction of human is considered to be one of the important factors affecting the epidemic transmission, and it is critical to model the heterogeneity of individual activities in epidemiological risk assessment. In digital society, massive data makes it possible to implement this idea on large scale. Here, we use the mobile phone signaling to track the users' trajectories and construct contact network to describe the topology of daily contact between individuals dynamically. We show the spatiotemporal contact features of about 7.5 million mobile phone users during the outbreak of COVID-19 in Shanghai, China. Furthermore, the individual feature matrix extracted from contact network enables us to carry out the extreme event learning and predict the regional transmission risk, which can be further decomposed into the risk due to the inflow of people from epidemic hot zones and the risk due to people close contacts within the observing area. This method is much more flexible and adaptive, and can be taken as one of the epidemic precautions before the large-scale outbreak with high efficiency and low cost.
(© 2022. The Author(s).)
Databáze: MEDLINE
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