tetano
Editor, Senior Moderator
BMC Public Health
. 2022 May 23;22(1):1030.
doi: 10.1186/s12889-022-13316-4.
Spatial differentiation and determinants of COVID-19 in Indonesia
Millary Agung Widiawaty[SUP] 1 2 [/SUP], Kuok Choy Lam[SUP] 3 [/SUP], Moh Dede[SUP] 2 4 [/SUP], Nur Hakimah Asnawi[SUP] 5 [/SUP]
Affiliations
Abstract
Background: The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors.
Methods: The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020.
Results: Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali.
Conclusion: Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.
Keywords: COVID-19; Socioenvironmental factors; Spatial interaction.
. 2022 May 23;22(1):1030.
doi: 10.1186/s12889-022-13316-4.
Spatial differentiation and determinants of COVID-19 in Indonesia
Millary Agung Widiawaty[SUP] 1 2 [/SUP], Kuok Choy Lam[SUP] 3 [/SUP], Moh Dede[SUP] 2 4 [/SUP], Nur Hakimah Asnawi[SUP] 5 [/SUP]
Affiliations
- PMID: 35606710
- DOI: 10.1186/s12889-022-13316-4
Abstract
Background: The spread of the coronavirus disease 2019 (COVID-19) has increasingly agonized daily lives worldwide. As an archipelagic country, Indonesia has various physical and social environments, which implies that each region has a different response to the pandemic. This study aims to analyze the spatial differentiation of COVID-19 in Indonesia and its interactions with socioenvironmental factors.
Methods: The socioenvironmental factors include seven variables, namely, the internet development index, literacy index, average temperature, urban index, poverty rate, population density (PD) and commuter worker (CW) rate. The multiple linear regression (MLR) and geographically weighted regression (GWR) models are used to analyze the impact of the socioenvironmental factors on COVID-19 cases. COVID-19 data is obtained from the Indonesian Ministry of Health until November 30th 2020.
Results: Results show that the COVID-19 cases in Indonesia are concentrated in Java, which is a densely populated area with high urbanization and industrialization. The other provinces with numerous confirmed COVID-19 cases include South Sulawesi, Bali, and North Sumatra. This study shows that the socioenvironmental factors, simultaneously, influence the increasing of confirmed COVID-19 cases in the 34 provinces of Indonesia. Spatial interactions between the variables in the GWR model are relatively better than those between the variables in the MLR model. The highest spatial tendency is observed outside Java, such as in East Nusa Tenggara, West Nusa Tenggara, and Bali.
Conclusion: Priority for mitigation and outbreak management should be high in areas with high PD, urbanized spaces, and CW.
Keywords: COVID-19; Socioenvironmental factors; Spatial interaction.