Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study

Abstract

Background The novel COVID-19 disease has spread worldwide, resulting in a new pandemic. The Chinese government implemented strong intervention measures in the early stage of the epidemic, including strict travel bans and social distancing policies. Prioritizing the analysis of different contributing factors to outbreak outcomes is important for the precise prevention and control of infectious diseases. We proposed a novel framework for resolving this issue and applied it to data from China. Objective This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China. Methods Daily COVID-19 case data and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and influenza-like illness factors, from 343 cities in China were collected. A correlation analysis and interpretable machine learning algorithm were used to evaluate the quantitative contribution of factors to new cases and COVID-19 growth rates during the epidemic period (ie, January 17 to February 29, 2020). Results Many factors correlated with the spread of COVID-19 in China. Travel-related population movement was the main contributing factor for new cases and COVID-19 growth rates in China, and its contributions were as high as 77% and 41%, respectively. There was a clear lag effect for travel-related factors (previous vs current week: new cases, 45% vs 32%; COVID-19 growth rates, 21% vs 20%). Travel from non-Wuhan regions was the single factor with the most significant impact on COVID-19 growth rates (contribution: new cases, 12%; COVID-19 growth rate, 26%), and its contribution could not be ignored. City flow, a measure of outbreak control strength, contributed 16% and 7% to new cases and COVID-19 growth rates, respectively. Socioeconomic factors also played important roles in COVID-19 growth rates in China (contribution, 28%). Other factors, including medical, environmental, and influenza-like illness factors, also contributed to new cases and COVID-19 growth rates in China. Based on our analysis of individual cities, compared to Beijing, population flow from Wuhan and internal flow within Wenzhou were driving factors for increasing the number of new cases in Wenzhou. For Chongqing, the main contributing factor for new cases was population flow from Hubei, beyond Wuhan. The high COVID-19 growth rates in Wenzhou were driven by population-related factors. Conclusions Many factors contributed to the COVID-19 outbreak outcomes in China. The differential effects of various factors, including specific city-level factors, emphasize the importance of precise, targeted strategies for controlling the COVID-19 outbreak and future infectious disease outbreaks.

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

“…The findings may help researchers and policymaker to implement effective responses in mitigating the consequences of the pandemic. Cao et al [134] applied XGBoost model to predict new COVID-19 cases and growth rate using six categories of variables, including travel-related factors, medical factors, socioeconomic factors, environmental factors and influenza-like illness factors. By calculating SHAP scores, the author quantitatively evaluated these contributing factors to new cases and growth rate.…”

“…Feature occlusion and ablation [28]- [31], [143], [144] SHAP feature importance [32]- [38], [96], [98], [102], [115]- [122], [134] Local interpretable model-agnostic explanations (LIME) [38]- [42] Activation-based Activation maximization [43] Class activation maps (CAM) [44], [45] Gradient-based…”

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Traditional Chinese medicine Lianhua Qingwen for treating COVID-19

“…It is not only because of measures taken by Chinese government at all levels, including preventing large public or private gatherings, wearing masks and gloves, and avoiding unnecessary travel and visitation, but also depends on the important factors of medical assistance. [ 1 – 3 ] In the pandemic, the Chinese government has provided valuable reference for countries around the world in epidemiology, diagnosis, and management, especially the TCM has played a huge role in the prevention and control of COVID-19. [ 4 ]…”

Modeling of the Long-Term Epidemic Dynamics of COVID-19 in the United States

“…Therefore, government decisions on policies and interventions will be essential in controlling the spread of COVID-19 in the following months. Non-pharmaceutical interventional measures including social distancing, mandatory mask-wearing, isolation, and contact tracing are still crucial in controlling the spread of the disease [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”