JMIR Publications

ABSTRACT

Background:

The novel coronavirus disease (COVID-19) spread world widely and caused a new pandemic, and countries around the world adopted various containment strategies. The Chinese government took strong intervention measures in the early stage of the epidemic, including strict travel-ban and social distancing policies. Various factors, including social, economic and environmental ones, contribute to the progression of the COVID-19 epidemic. Prioritizing contribution of different factors is important for precise prevention and control of infectious diseases. Here, we proposed a novel framework for resolving this question and applied it to data from China.

Objective:

To systematically reveal factors and their contribution to the control of COVID-19 in China, both at the national and city level.

Methods:

Daily COVID-19 cases and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and immune-related factors, from 343 cities in China were collected. Correlation analysis and machine learning algorithm were used to explore the quantitative contribution of different factors on either new cases or growth rate of COVID-19 for the period from January 17 to February 29, 2020.

Results:

Many factors considered in this study are correlated to the spread of COVID-19 in China. Overall, travel-related population movements are the main contributing factors for both new cases and growth rate of COVID-19 in China, and the contributions are as high as 77% and 41%, respectively. Specifically, population flow from Wuhan and from regions beyond Wuhan are the leading travel-related factors for new cases and growth rate with contribution of 49% and 24%. Socioeconomic factors also play important roles in the growth rate of COVID-19 in China with 28% contribution. Compared to Beijing, population flow from Wuhan and internal flow within the city are driving factors for more new cases in Wenzhou, while for Chongqing the contribution is mainly from population flow from Hubei excluding Wuhan. The higher growth rate for Wenzhou is driven by its population related factors.

Conclusions:

Travel-related population movement is the main driving factor for the outbreak outcome of COVID-19 in China. For the growth rate, more factors are involved, including the socioeconomic ones. Differences in outbreak outcome for different cities could be explained by specific factors, which emphasizes the importance for personalized strategies for disease control.