Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China

Youliang Chen,Xunjun Chen,Qun Li,Xiaoming Li,Hamed Karimian

doi:10.1038/s41598-021-83166-4

Abstract

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China’s non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics.

Highlights

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China
The results show that the overall trend of daily new confirmed cases in China and Hubei Province was consistent with the time distribution
In order to better understanding of the changes in non-Hubei provinces, we divided the daily COVID-19 change curve for non-Hubei provinces into 5 stages: stage I: Before January 22, this phase is the initial stage of the COVID-19 outbreak in which the number of confirmed cases is relatively small, the daily new cases are more moderate; stage II: From January 23 to February 3, due to the spread way of the COVID-19, the disease spread rapidly and the daily new cases reached the first peak on January 31

Summary

Introduction

In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. Assessing individual NPIs, without considering other interventions, may not give a right view related to effectiveness of NPIs. In order to control the outbreak, the Chinese government has introduced a series of prevention and control measures, such as extending the Spring festival holiday, postponing the resumption of work and school, traffic control and calling for home isolation measures, in both national and local level. Other studies came to an opposite conclusion that meteorological factors, such as temperature and relative humidity, were associated with COVID-19 cases. Menebo et al.[16] analyzed the correlation between metrological factors and COVID-19 prevalence in the Norwegian capital Oslo and found that the highest temperature and normally mild precipitation have influence on distribution of the virus.

Methods

Results

Conclusion