Introduction: In order to study the impact of social factors on the evolution of the epidemic, this paper takes the COVID-19 in Hubei Province of China as an example to study the impact of social factors such as the permanent population, universities, hospitals, the distance between Wuhan seafood market and 17 cities in Hubei Province, and the distribution of medical supplies on the COVID-19. This is of great significance for helping to develop effective prevention and control measures and response strategies, ensuring public health and social stability.
Methods: Time series regression analysis is used to study the impact of various factors on the epidemic situation, multidimensional scale analysis is used to assess the differences among provinces, and Almon polynomial is used to study the lag effect of the impact.
Results: We found that these cities can be divided into three groups based on the number of confirmed cases and the time course data of the cases. The results verify that these factors have a great impact on the evolution of the COVID-19.
Discussion: With the increase in the number of universities, the number of confirmed cases and new cases has significantly increased. With the increase in population density, the number of new cases has significantly increased. In addition, the farther away from the Wuhan seafood market, the fewer confirmed cases. It is worth noting that the insufficient increase in medical supplies in some cities still leads to a significant increase in new cases. This impact is regional, and their lag periods are also different. Through the comparison with Guangdong Province, it is concluded that social factors will affect COVID-19. Overall, promoting the construction of medical schools and ensuring the reasonable distribution of medical supplies is crucial as it can effectively assist decision-making.
Keywords: evolution of the COVID-19; lag period; linear model; social factors; statistical analysis; time-series data.
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