印度新冠肺炎死亡率的空间映射及社会人口学决定因素
Spatial mapping and socio-demographic determinants of COVID-19 mortality in India.
作者信息
Khobragade Ashish W, Kadam Dilip D
机构信息
Department of Community and Family Medicine, All India Institute of Medical Sciences, Raipur, Chhattisgarh, India.
Department of Community Medicine, Seth G.S. Medical College, Mumbai, Maharashtra, India.
出版信息
J Family Med Prim Care. 2021 Nov;10(11):4200-4204. doi: 10.4103/jfmpc.jfmpc_903_21. Epub 2021 Nov 29.
BACKGROUND
COVID-19 is caused by SARS-CoV-2. The first case of COVID-19 was detected in Wuhan city of China in December 2019. Geographic information system (GIS) mapping is important for the surveillance of infectious diseases.
OBJECTIVES
The objectives of the study are to map spatially total cases and case fatality rate of COVID-19 and to build a linear regression model for mortality based on socio-demographic variables.
METHOLOGY
We plotted the epidemiological data of COVID-19 of Indian states as on 11 May 2021 using the Q-GIS software. We used socio-demographic variables as the predictors of COVID-19 mortality and developed a linear regression model.
RESULTS
Adjusted R-squared in linear regression model based on socio-demographic variables for COVID-19 deaths is 0.82.
CONCLUSIONS
There are spatial variations in COVID-19 cases and deaths.
背景
新型冠状病毒肺炎（COVID-19）由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起。2019年12月在中国武汉市发现了首例COVID-19病例。地理信息系统（GIS）绘图对传染病监测很重要。
目的
本研究的目的是对COVID-19的总病例数和病死率进行空间绘图，并基于社会人口统计学变量建立死亡率线性回归模型。
方法
我们使用Q-GIS软件绘制了截至2021年5月11日印度各邦COVID-19的流行病学数据。我们将社会人口统计学变量用作COVID-19死亡率的预测因子，并建立了线性回归模型。
结果
基于社会人口统计学变量的COVID-19死亡线性回归模型的调整R平方为0.82。
结论
COVID-19病例和死亡存在空间差异。
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