Exploring the Pattern of Early COVID-19 Transmission Caused by Population Migration Based on 14 Cities in Hubei Province, China

Background 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 \documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$y = 14.349{e^{0.085025x}} + 298.22$$ \end{document}. In each of the fourteen cities, The nonlinear exponential model structure is as follows:\documentclass[12pt]{minimal} \usepackage{wasysym} \usepackage[substack]{amsmath} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage[mathscr]{eucal} \usepackage{mathrsfs} \DeclareFontFamily{T1}{linotext}{} \DeclareFontShape{T1}{linotext}{m}{n} {linotext }{} \DeclareSymbolFont{linotext}{T1}{linotext}{m}{n} \DeclareSymbolFontAlphabet{\mathLINOTEXT}{linotext} \begin{document} $$y = a{e^{bx}} + c,a \ne 0,{\rm{b}} \ne 0$$ \end{document}. 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.