Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China

Ying Wang, Fang Yuan, Yueqian Song, Huaxiang Rao, Lili Xiao, Huilin Guo, Xiaolong Zhang, Mufan Li, Jiayu Wang, Yi zhou Ren, Jie Tian and Jianzhou Yang

PLOS ONE, 2024, vol. 19, issue 4, 1-21

Abstract: The COVID-19 pandemic has been present globally for more than three years, and cross-border transmission has played an important role in its spread. Currently, most predictions of COVID-19 spread are limited to a country (or a region), and models for cross-border transmission risk assessment remain lacking. Information on imported COVID-19 cases reported from March 2020 to June 2022 was collected from the National Health Commission of China, and COVID-19 epidemic data of the countries of origin of the imported cases were collected on data websites such as WHO and Our World in Data. It is proposed to establish a prediction model suitable for the prevention and control of overseas importation of COVID-19. Firstly, the SIR model was used to fit the epidemic infection status of the countries where the cases were exported, and most of the r2 values of the fitted curves obtained were above 0.75, which indicated that the SIR model could well fit different countries and the infection status of the region. After fitting the epidemic infection status data of overseas exporting countries, on this basis, a SIR-multiple linear regression overseas import risk prediction combination model was established, which can predict the risk of overseas case importation, and the established overseas import risk model overall P

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0301420

DOI: 10.1371/journal.pone.0301420

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