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

Jumadi Jumadi (), Fikriyah Vidya N., Hadibasyir Hamim Zaky, Priyono Kuswaji Dwi, Musiyam Muhammad, Mardiah Andri N. R., Rohman Arif, Hasyim Hamzah and Ibrahim Mohd. Hairy
Additional contact information
Jumadi Jumadi: Faculty of Geography, Universitas Muhammadiyah Surakarta, Indonesia
Fikriyah Vidya N.: Faculty of Geography, Universitas Muhammadiyah Surakarta, Indonesia
Hadibasyir Hamim Zaky: Faculty of Geography, Universitas Muhammadiyah Surakarta, Indonesia
Priyono Kuswaji Dwi: Faculty of Geography, Universitas Muhammadiyah Surakarta, Indonesia
Musiyam Muhammad: Department of Geography Education, Universitas Muhammadiyah Surakarta, Indonesia
Mardiah Andri N. R.: The Ministry of National Development Planning/BAPPENAS, Indonesia
Rohman Arif: Geomatic Engineering, Institut Teknologi Sumatera (ITERA), Indonesia
Hasyim Hamzah: Faculty of Public Health, Universitas Sriwijaya, South Sumatra Province, Indonesia
Ibrahim Mohd. Hairy: Universiti Pendidikan Sultan Idris, Malaysia

Quaestiones Geographicae, 2022, vol. 41, issue 2, 139-151

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.

Keywords: COVID-19; spatio-temporal analysis; epidemic mapping; pandemic; Indonesia (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2478/quageo-2022-0013 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:quageo:v:41:y:2022:i:2:p:139-151:n:1

DOI: 10.2478/quageo-2022-0013

Access Statistics for this article

Quaestiones Geographicae is currently edited by Andrzej Kostrzewski

More articles in Quaestiones Geographicae from Sciendo
Bibliographic data for series maintained by Peter Golla ().