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

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...

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Published in:	PloS one 2024-04, Vol.19 (4), p.e0301420-e0301420
Main Authors:	Wang, Ying, Yuan, Fang, Song, Yueqian, Rao, Huaxiang, Xiao, Lili, Guo, Huilin, Zhang, Xiaolong, Li, Mufan, Wang, Jiayu, Ren, Yi Zhou, Tian, Jie, Yang, Jianzhou
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Language:	English
Subjects:	China China - epidemiology Control COVID-19 - epidemiology Epidemics Health aspects Health risk assessment Humans Linear Models Medicine and Health Sciences Methods Pandemics Physical Sciences Research and Analysis Methods Risk assessment SARS-CoV-2 Travel restrictions
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description	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
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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 <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. 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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2024 Public Library of Science</rights><rights>2024 Wang et al 2024 Wang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c590t-f2b6f2e64d4f9d4c94b0325f9408a46716069a3f2cd97cdbe11105133dd8286a3</cites><orcidid>0000-0001-5354-2290 ; 0000-0002-6388-8276</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003692/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11003692/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,37013,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38593140$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Filipovic, Nenad</contributor><creatorcontrib>Wang, Ying</creatorcontrib><creatorcontrib>Yuan, Fang</creatorcontrib><creatorcontrib>Song, Yueqian</creatorcontrib><creatorcontrib>Rao, Huaxiang</creatorcontrib><creatorcontrib>Xiao, Lili</creatorcontrib><creatorcontrib>Guo, Huilin</creatorcontrib><creatorcontrib>Zhang, Xiaolong</creatorcontrib><creatorcontrib>Li, Mufan</creatorcontrib><creatorcontrib>Wang, Jiayu</creatorcontrib><creatorcontrib>Ren, Yi Zhou</creatorcontrib><creatorcontrib>Tian, Jie</creatorcontrib><creatorcontrib>Yang, Jianzhou</creatorcontrib><title>Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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 <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. 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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 <0.05, the adjusted R2 = 0.7, indicating that the SIR-multivariate linear regression overseas import risk prediction combination model can obtain better prediction results. Our model effectively estimates the risk of imported cases of COVID-19 from abroad.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>38593140</pmid><doi>10.1371/journal.pone.0301420</doi><tpages>e0301420</tpages><orcidid>https://orcid.org/0000-0001-5354-2290</orcidid><orcidid>https://orcid.org/0000-0002-6388-8276</orcidid><oa>free_for_read</oa></addata></record>
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subjects	China China - epidemiology Control COVID-19 - epidemiology Epidemics Health aspects Health risk assessment Humans Linear Models Medicine and Health Sciences Methods Pandemics Physical Sciences Research and Analysis Methods Risk assessment SARS-CoV-2 Travel restrictions
title	Prediction of cross-border spread of the COVID-19 pandemic: A predictive model for imported cases outside China
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