Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study.
Impact of Systematic Factors on the Outbreak Outcomes of the Novel COVID-19 Disease in China: Factor Analysis Study.
复制标题
DOI：
10.2196/23853
复制
发表时间：
2020-11-11
影响因子：
7.4
通讯作者：
Du X
中科院分区：
文献类型：
--
作者：
Cao Z;Tang F;Chen C;Zhang C;Guo Y;Lin R;Huang Z;Teng Y;Xie T;Xu Y;Song Y;Wu F;Dong P;Luo G;Jiang Y;Zou H;Chen YQ;Sun L;Shu Y;Du X
新型COVID-19疾病已在全球蔓延，导致新的大流行。中国政府在疫情初期实施了强有力的干预措施，包括严格的旅行禁令和社交距离政策。优先分析导致疫情结果的不同促成因素对于传染病的精确预防和控制非常重要。我们提出了一个新的框架来解决这个问题，并将其应用于中国的数据。本研究旨在系统地识别中国控制COVID-19的国家级和城市级影响因素。收集了中国343个城市的每日COVID-19病例数据和相关多维数据，包括旅行相关、医疗、社会经济、环境和流感样疾病因素。使用相关性分析和可解释的机器学习算法来评估流行期间（即2020年1月17日至2月29日）因素对新病例和COVID-19增长率的定量贡献。许多因素与COVID-19在中国的传播有关。与旅行相关的人口流动是中国新增病例和COVID-19增长率的主要因素，其贡献分别高达77%和41%。旅行相关因素存在明显的滞后效应（前一周与当前一周：新病例，45%与32%; COVID-19增长率，21%与20%）。来自非武汉地区的旅行是对COVID-19增长率影响最显著的单一因素（贡献：新增病例，12%; COVID-19增长率，26%），其贡献不容忽视。城市流量是衡量疫情控制力度的指标，对新病例和COVID-19增长率的贡献分别为16%和7%。社会经济因素也在中国COVID-19增长率中发挥了重要作用（贡献率为28%）。其他因素，包括医疗、环境和流感样疾病因素，也导致了中国的新病例和COVID-19增长率。根据我们对单个城市的分析，与北京相比，来自武汉的人口流动和温州内部的流动是温州新增病例数量增加的驱动因素。对于重庆来说，新增病例的主要影响因素是武汉以外的湖北人口流动。温州的COVID-19高增长率是由人口相关因素推动的。多种因素导致COVID-19在中国爆发的结果。各种因素（包括特定城市层面的因素）的不同影响强调了精确、有针对性的策略对控制COVID-19疫情和未来传染病疫情的重要性。
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. This study aimed to systematically identify national-level and city-level contributing factors to the control of COVID-19 in China. 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). 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. 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.
登录
查看更多内容
影响因子：
4.6
作者：
Price, Rory Henry Macgregor;Graham, Catriona;Ramalingam, Sandeep
通讯作者：
Ramalingam, Sandeep
影响因子：
28.3
作者：
Kong, Wen-Hua;Li, Yao;Liu, Man-Qing
通讯作者：
Liu, Man-Qing
影响因子：
28.3
作者：
Gorbalenya, Alexander E.;Baker, Susan C.;Ziebuhr, John
通讯作者：
Ziebuhr, John
DOI：
10.1016/j.scitotenv.2020.138778
发表时间：
2020-08-01
期刊：
The Science of the total environment
影响因子：
--
作者：
Qi, Hongchao;Xiao, Shuang;Zhang, Zhijie
通讯作者：
Zhang, Zhijie
影响因子：
3.8
作者：
Mitchell, Rory;Frank, Eibe
通讯作者：
Frank, Eibe