<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the spatio-temporal trend of COVID-19 cases and fatalities to get a better understanding of the spread as well as to mitigate it. However, such a comprehensive study at national level is not to be seen in Indonesia with limited health infrastructure. This study aims to analyse the spatio-temporal distribution and clusters of COVID-19 in Indonesia for a year period. COVID-19 cases, as well as the fatalities as a consequence of this disease, were collected from the government through publicly shared data. A geographic information system (GIS) was used to manage and analyse the data on demographics, cases, and fatalities. The case fatality rate (CFR) was produced based on the number of cases and deaths per province weekly. The spatio-temporal data of both cases and fatalities were generated from the data. Finally, K-means clustering was employed to classify the cluster of Indonesia based on the proportion of vulnerable age groups, cases, and CFR. The results show that most of the provinces in Indonesia are affected by COVID-19, but the fatalities are not distributed evenly throughout the country. Based on the K-means clustering, two provinces are classified as moderate, namely the Province of East Kalimantan and North Kalimantan. The Province of Jakarta is classified as high, because the vulnerable age group there is highly correlated with the number of cases and deaths.</p>
</abstract>

Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the spatio-temporal trend of COVID-19 cases and fatalities to get a better understanding of the spread as well as to mitigate it. However, such a comprehensive study at national level is not to be seen in Indonesia with limited health infrastructure. This study aims to analyse the spatio-temporal distribution and clusters of COVID-19 in Indonesia for a year period. COVID-19 cases, as well as the fatalities as a consequence of this disease, were collected from the government through publicly shared data. A geographic information system (GIS) was used to manage and analyse the data on demographics, cases, and fatalities. The case fatality rate (CFR) was produced based on the number of cases and deaths per province weekly. The spatio-temporal data of both cases and fatalities were generated from the data. Finally, K-means clustering was employed to classify the cluster of Indonesia based on the proportion of vulnerable age groups, cases, and CFR. The results show that most of the provinces in Indonesia are affected by COVID-19, but the fatalities are not distributed evenly throughout the country. Based on the K-means clustering, two provinces are classified as moderate, namely the Province of East Kalimantan and North Kalimantan. The Province of Jakarta is classified as high, because the vulnerable age group there is highly correlated with the number of cases and deaths.

A Year of Spatio-Temporal Clusters of COVID-19 in Indonesia

Quaestiones Geographicae

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A Year of Spatio-Temporal Clusters of COVID-19 in Indonesia"}

Coronavirus disease-2019 (COVID-19) in Indonesia began to appear on March 2, 2020 and led to a number of fatalities. Spatial analysis is important to study the...

A Year of Spatio-Temporal Clusters of COVID-19 in Indonesia

Published Online: Jun 24, 2022

Page range: 139 - 151

Received: Jul 12, 2021

DOI: https://doi.org/10.2478/quageo-2022-0013

Keywords
COVID-19, spatio-temporal analysis, epidemic mapping, pandemic, Indonesia

© 2022 Jumadi Jumadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A Year of Spatio-Temporal Clusters of COVID-19 in Indonesia

Published Online: Jun 24, 2022

Page range: 139 - 151

Received: Jul 12, 2021

DOI: https://doi.org/10.2478/quageo-2022-0013

KeywordsCOVID-19, spatio-temporal analysis, epidemic mapping, pandemic, Indonesia

© 2022 Jumadi Jumadi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
COVID-19, spatio-temporal analysis, epidemic mapping, pandemic, Indonesia