Your browser doesn't support javascript.
Machine learning based regional epidemic transmission risks precaution in digital society.
Shi, Zhengyu; Qian, Haoqi; Li, Yao; Wu, Fan; Wu, Libo.
  • 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.
  • Li Y; LSE-Fudan Research Centre for Global Public Policy, Fudan University, Shanghai, 200433, China. qianhaoqi@fudan.edu.cn.
  • Wu F; MOE Laboratory for National Development and Intelligent Governance, Fudan University, Shanghai, 200433, China. qianhaoqi@fudan.edu.cn.
  • Wu L; Shanghai Ideal Information Industry (Group) Co., Ltd, Fudan University, Shanghai, 200120, China.
Sci Rep ; 12(1): 20499, 2022 Nov 28.
Article in English | MEDLINE | ID: covidwho-2133631
ABSTRACT
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 / Names Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-24670-z

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Epidemics / COVID-19 / Names Type of study: Observational study / Prognostic study Limits: Humans Country/Region as subject: Asia Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-24670-z