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Lin Luo,1– 3 Wen Wen,4 Chun-yi Wang,4 Mengyun Zhou,5 Jie Ni,6 Jingjie Jiang,4 Juan Chen,4 Ming-wei Wang,4 Zhanhui Feng,6 Yong-Ran Cheng7 1Hangzhou Ruolin Hospital Management Co. Ltd, Hangzhou, 310007, People’s Republic of China; 2Hangzhou Kaihong Technology Co., Ltd, Hangzhou, 310059, People’s Republic of China; 3Jiangxi Key Laboratory of Natural Products and Functional Food, College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang, 330045, People’s Republic of China; 4Hangzhou Institute of Cardiovascular Diseases, Hangzhou Medical Key Discipline, Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, People’s Republic of China; 5Department of Molecular & Cellular Physiology, Shinshu University School of Medicine, Nagano, 3900803, Japan; 6Internal Medicine-Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, People’s Republic of China; 7School of Public Health, Hangzhou Medical College, Hangzhou, 311300, People’s Republic of ChinaCorrespondence: Yong-Ran Cheng; Zhanhui Feng Email chengyr@zjams.com.cn; h9450203@126.comBackground and Aim: Relevant studies show that population migration has a great impact on the early spread of infectious diseases. Therefore, it is important to explore whether there is an explicit relationship between population migration and the number of confirmed cases for the control of the COVID-19 epidemic. This paper mainly explores the impact of population migration on early COVID-19 transmission, and establishes a predictive nonlinear mathematical model to predict the number of early cases.Methods: Data of confirmed cases were sourced from the official website of the Municipal Health Committee, and the proportions of migration from Wuhan to other cities were sourced from the Baidu data platform. The data of confirmed cases and the migration proportions of 14 cities in Hubei Province were collected, the COVID-19 cases study period was determined as 10 days based on the third quartile of the interval of the incubation period, and a non-linear mathematical model was constructed to clarify the relationship between the migration proportion and the number of confirmed COVID-19 cases. Finally, eight typical regions were selected to verify the accuracy of the model.Results: The daily population migration rates and the growth curves of the number of confirmed cases in the 14 cities were basically consistent, and Pearson’s correlation coefficient was 0.91. The specific mathematical expression of 14 regions is . In each of the fourteen cities, The nonlinear exponential model structure is as follows:. It was found that the R2 values of the fitted mathematical model were greater than 0.8 in all studied regions, excluding Suizhou (p < 0.05). The established mathematical model was used to fit eight regions in China, and the correlations between the predicted and actual numbers of confirmed cases were greater than 0.9, excluding that of Hebei Province (0.82).Conclusion: The study found that population migration has a positive and significant impact on the spread of COVID-19. Modeling COVID-19 risk may be a useful strategy for directing public health surveillance and interventions. Restricting the migration of the population is of great significance to the joint prevention and control of the pandemic worldwide.Keywords: COVID-19, SARS-CoV-2, spreads, travel, non-linear exponential |
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