Diseases maps of spatial epidemiological data by R

IF 4.4 2区 数学 Q1 STATISTICS & PROBABILITY
T. Kubota
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引用次数: 0

Abstract

Disease maps are essential when analyzing spatial epidemiological data, such as newly detected COVID‐19 positive cases or suicide deaths, because it is necessary to determine the method of analysis in order to perform spatial statistical analysis. Disease maps give an initial overview of the data and provide evidence of regional trends, which the analyst can check. Therefore, in this article, the author aimed to use R, a statistical data analysis tool, to draw spatial epidemiological data in the form of disease maps. This article presents three different methods and analyzes recent trends in COVID‐19 and suicide mortality. The author used monthly data from April, July, and October 2020. The results showed no significant trend in April, but some prefectures showed a negative correlation in July. On the other hand, some prefectures showed a positive correlation in October, confirming the influence of COVID‐19 on suicide by region.
空间流行病学数据的疾病地图
在分析空间流行病学数据(如新发现的COVID - 19阳性病例或自杀死亡)时,疾病地图至关重要,因为有必要确定分析方法,以便进行空间统计分析。疾病地图提供了数据的初步概述,并提供了分析人员可以检查的区域趋势的证据。因此,在本文中,作者旨在使用统计数据分析工具R,以疾病地图的形式绘制空间流行病学数据。本文提出了三种不同的方法,并分析了COVID - 19和自杀死亡率的最新趋势。作者使用了2020年4月、7月和10月的月度数据。结果显示,4月份没有明显的趋势,但部分地区在7月份出现负相关。另一方面,一些县在10月份表现出正相关,证实了COVID - 19对地区自杀的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.20
自引率
0.00%
发文量
31
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