Using mathematical model to predict the transmission trend of COVID-19 infection in global scale

Lingming Kong; Guanlin Ou; Tang Tian

doi:10.1117/12.2686106

28 July 2023 Using mathematical model to predict the transmission trend of COVID-19 infection in global scale

Lingming Kong, Guanlin Ou, Tang Tian

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127560U (2023) https://doi.org/10.1117/12.2686106
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

A deterministic model of COVID-19 transmission is established by using a differential equation system, which integrates the protective adaptive behavior of human population and the protective effect of vaccine. Based on the data of reported cases and vaccinations from February 1, 2020 to July 16, 2022, parameter fitting is performed by MCMC method to estimate the intervention effect and transmission trend. The results indicate that (1) this epidemic may level off around October 2021 if without any intervention, but in big cost of disease burden, leading to 5.46 billion cases (about 10 times of current infections); (2) 210 million people may be free of infection due to vaccination, which is 37% of the current number of infections; and (3) according to the current situation of intervention measures, the cumulative number of cases in the world would slowly increase and tend to be flat by 2024. The epidemic will be completely ended in February 2026, and the cumulative number of cases will reach 870 million. The findings show that protective behavior, vaccination and its protection rate are still the key factors to determine the global epidemic trend.

Citation Download Citation

Lingming Kong, Guanlin Ou, and Tang Tian "Using mathematical model to predict the transmission trend of COVID-19 infection in global scale", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127560U (28 July 2023); https://doi.org/10.1117/12.2686106

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