Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Apr 9;19(4):e0301420.
doi: 10.1371/journal.pone.0301420. eCollection 2024.

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

Ying Wang  1   2   3 , Fang Yuan  1 , Yueqian Song  1 , Huaxiang Rao  3 , Lili Xiao  1 , Huilin Guo  1 , Xiaolong Zhang  1 , Mufan Li  2 , Jiayu Wang  2 , Yi Zhou Ren  2 , Jie Tian  1 , Jianzhou Yang  3
Affiliations

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

Ying Wang et al. PLoS One. .

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 <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.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig 1
Fig 1. SIR model infection process.
Fig 2
Fig 2. One of the schematic diagrams of the I(t) peak interval of MMR.
Fig 3
Fig 3. Schematic diagram of optimizing the SIR model parameters.
Fig 4
Fig 4. Schematic diagram of the parameter-optimized fitting results.
Fig 5
Fig 5. Schematic diagram of the prediction process.
Fig 6
Fig 6. Comparison of the actual infected persons among different Asian countries based on SIR fitting results.
Fig 7
Fig 7. Comparison of the actual infected persons among different European countries based on SIR fitting results.
Fig 8
Fig 8. Comparison of the actual infected persons among different African countries based on SIR fitting results.
Fig 9
Fig 9. Comparison of the actual infected persons among different Americas and Oceania countries based on SIR fitting results.
Fig 10
Fig 10. R2 density plot of SIR model goodness of fit for all infection curves in 51 countries.
Fig 11
Fig 11. qq-plot plot of the residuals for the training set.
Fig 12
Fig 12. qq-plot plot of the residuals for the test set.
Fig 13
Fig 13. SIR-multiple linear regression model 2022 forecast set data forecast results chart.
Fig 14
Fig 14. qq-plot of the predicted set residuals in 2022.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Wang Z, Fu Y, Guo Z, Li J, Li J, Cheng H, et al.. Transmission and prevention of SARS-CoV-2. Biochemical Society Transactions. 2020;48(5):2307–16. doi: 10.1042/BST20200693 - DOI - PubMed
    1. Organization WH. WHO Coronavirus (COVID-19) Dashboard | WHO Coronavirus (COVID-19) Dashboard With Vaccination Data https://covid19.who.int/
    1. Zhan C, Tse CK, Fu Y, Lai Z, Zhang H. Modeling and prediction of the 2019 coronavirus disease spreading in China incorporating human migration data. PLoS One. 2020;15(10):e0241171. Published 2020 Oct 27. doi: 10.1371/journal.pone.0241171 - DOI - PMC - PubMed
    1. Zhan C, Zheng Y, Shao L, Chen G, Zhang H. Modeling the spread dynamics of multiple-variant coronavirus disease under public health interventions: A general framework. Inf Sci (N Y). 2023;628:469–487. doi: 10.1016/j.ins.2023.02.001 - DOI - PMC - PubMed
    1. Nyasulu JCY, Munthali RJ, Nyondo-Mipando AL, Pandya H, Nyirenda L, Nyasulu PS, et al.. COVID-19 pandemic in Malawi: Did public sociopolitical events gatherings contribute to its first-wave local transmission? Int J Infect Dis. 2021;106:269–75. doi: 10.1016/j.ijid.2021.03.055 - DOI - PMC - PubMed