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印度尼西亚西爪哇省 COVID-19 病例的时空分析及其影响因素。

Spatial and Temporal Analysis of COVID-19 Cases in West Java, Indonesia and Its Influencing Factors.

机构信息

Epidemiology Study Program, Faculty of Medicine, Universitas Padjadjaran, Jalan Eyckman No. 38 Gedung RSP Unpad Lantai 4, Bandung 40161, Indonesia.

Division Epidemiology and Biostatistics, Department of Public Health, Faculty of Medicine, Universitas Padjadjaran, Jalan Ir. Soekarno KM. 21, Jatinangor, Sumedang 45363, Indonesia.

出版信息

Int J Environ Res Public Health. 2023 Feb 11;20(4):3198. doi: 10.3390/ijerph20043198.

DOI:10.3390/ijerph20043198
PMID:36833893
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9960347/
Abstract

Coronavirus Disease 2019 (COVID-19) spread quickly and reached epidemic levels worldwide. West Java is Indonesia's most populous province and has a high susceptibility to the transmission of the disease, resulting in a significant number of COVID-19 cases. Therefore, this research aimed to determine the influencing factors as well as the spatial and temporal distribution of COVID-19 in West Java. Data on COVID-19 cases in West Java obtained from PIKOBAR were used. Spatial distribution was described using a choropleth, while the influencing factors were evaluated with regression analysis. To determine whether COVID-19s policies and events affected its temporal distribution, the cases detected were graphed daily or biweekly with information on those two variables. Furthermore, the cumulative incidence was described in the linear regression analysis model as being significantly influenced by vaccinations and greatly elevated by population density. The biweekly chart had a random pattern with sharp decreases or spikes in cumulative incidence changes. Spatial and temporal analysis helps greatly in understanding distribution patterns and their influencing factors, specifically at the beginning of the pandemic. Plans and strategies for control and assessment programs may be supported by this study material.

摘要

2019 年冠状病毒病（COVID-19）迅速传播，在全球范围内达到流行水平。西爪哇是印度尼西亚人口最多的省份，疾病传播的易感性很高，导致 COVID-19 病例数量众多。因此，本研究旨在确定西爪哇 COVID-19 的影响因素以及时空分布。使用从 PIKOBAR 获得的西爪哇 COVID-19 病例数据。使用面域图描述空间分布，并用回归分析评估影响因素。为了确定 COVID-19 的政策和事件是否影响其时间分布，每天或每两周以关于这两个变量的信息绘制病例图。此外，线性回归分析模型表明，累积发病率受到疫苗接种的显著影响，并因人口密度的增加而大大升高。两周图表具有随机模式，累积发病率变化急剧下降或出现峰值。时空分析有助于极大地了解分布模式及其影响因素，特别是在大流行初期。本研究材料可以为控制和评估计划的计划和策略提供支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/e73c39aa4758/ijerph-20-03198-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/09324a4676f8/ijerph-20-03198-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/81935bf8e3f1/ijerph-20-03198-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/9c910261e038/ijerph-20-03198-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/ff28fa597c8a/ijerph-20-03198-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/9747fcd0bcfb/ijerph-20-03198-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/f5c3a3a775ac/ijerph-20-03198-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/60f3ee756611/ijerph-20-03198-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/cca04ea1718c/ijerph-20-03198-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/c2aa08679f53/ijerph-20-03198-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/e73c39aa4758/ijerph-20-03198-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/09324a4676f8/ijerph-20-03198-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/81935bf8e3f1/ijerph-20-03198-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/9c910261e038/ijerph-20-03198-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/ff28fa597c8a/ijerph-20-03198-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/9747fcd0bcfb/ijerph-20-03198-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/f5c3a3a775ac/ijerph-20-03198-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/60f3ee756611/ijerph-20-03198-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/cca04ea1718c/ijerph-20-03198-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/c2aa08679f53/ijerph-20-03198-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b9e/9960347/e73c39aa4758/ijerph-20-03198-g010.jpg

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