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

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 ris

SUBMITTER: Shi Z 

PROVIDER: S-EPMC9705289 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature