Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China.
Spatio-temporal distribution characteristics and influencing factors of COVID-19 in China.
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DOI：
10.1038/s41598-021-83166-4
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发表时间：
2021-02-12
影响因子：
4.6
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
Chen Y;Li Q;Karimian H;Chen X;Li X
于二零一九年十二月，中国爆发2019冠状病毒病（COVID-19）。了解国家层面的疾病分布有助于制定公共卫生政策。有几项研究调查了COVID-19在中国分布的影响因素。然而，需要考虑更多的影响因素，以提高我们对当前疫情的认识。此外，在缺乏有效药物或疫苗的情况下，中国政府推出了一系列非药物干预措施（NPI）。然而，评估和预测这些干预措施的有效性需要进一步研究。本文运用统计技术、相关分析和GIS制图表达方法，分析了中国大陆COVID-19疫情的时空分布特征及其影响因素。结果表明，中国非湖北省暴发疫情的传播可分为五个阶段。第一阶段是COVID-19疫情爆发的初始阶段;第二阶段是新的疫情高峰;第三阶段是疫情得到控制，新增病例减少;第四阶段是反弹，第五阶段是平稳。不仅如此，累计确诊病例主要集中在中国东南部地区，来自武汉的人口流动较大的城市疫情较为严重。此外，流行与温度、降雨量和相对湿度之间存在统计学显著相关。为了评估NPI，我们基于改进的SIR模型和不同的预防强度模拟了COVID-19的流行情况。结果表明，本文的模拟结果与实测值吻合较好，改进后的SIR模型的时间函数参数为a =-0.0058。本研究的结果和方法可有效地预测和管理流行病，并可作为决策者控制当前和未来流行病的辅助手段。
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.
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