Effects of COVID-19 restriction measures in Indonesia: A comparative spatial and policy analysis of selected urban agglomerations
With higher densities, urban agglomerations account for the fastest rates of COVID-19 transmission. In Indonesia, one of the most rapidly urbanizing regions in the world, the national government issues overall policy on the pandemic. However, implementation is often contingent on local governments....
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| Published in: | International journal of disaster risk reduction Vol. 76; p. 103015 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
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England
Elsevier Ltd
15.06.2022
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| ISSN: | 2212-4209, 2212-4209 |
| Online Access: | Get full text |
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| Abstract | With higher densities, urban agglomerations account for the fastest rates of COVID-19 transmission. In Indonesia, one of the most rapidly urbanizing regions in the world, the national government issues overall policy on the pandemic. However, implementation is often contingent on local governments. Many policies aim to stem the spread of infection by controlling people's mobility or regulating their daily activities. Urban agglomerations are a strategic site of investigation in this light, because they consist of interconnected communities governed by various levels and jurisdictions. This paper analyzes the effects of policy interventions relative to confirmed cases in the seven major urban agglomerations in Indonesia (totaling 30 municipal/district governments). Data were collected from confirmed and fatality trends from March to mid-October 2020, which were contrasted with corresponding policies for each jurisdiction. By sorting the indicators of the spread of the pandemic and its corresponding control measures, we reach conclusions about which dimensions served to curb or trigger the surge of COVID-19 clusters. The analysis unsurprisingly shows that within each agglomeration, the main cities continue to represent the highest number of confirmed cases despite variations between them. This study also highlights two key findings. First, the effectiveness of distancing measures depends considerably on the capacity of governments to implement restrictions. For example, budget limitations resulted in uneven implementation of national mandates by decentralized authority. Facilities and services at different locations also influence our understanding of disease transmission. Second, people's ability and willingness to engage with a policy regime is contingent upon personal values or economic constraints. The study shows that viewing the spatial distribution of COVID-19 at the scale of urban agglomerations helps to explain key aspects of transmission and policy, pointing to recommendations about pursuing certain protocols. Nevertheless, key challenges remain to meet the full potential of this analytical approach, due to relatively low levels of testing and inadequate data collection measures in Indonesia. |
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| AbstractList | With higher densities, urban agglomerations account for the fastest rates of COVID-19 transmission. In Indonesia, one of the most rapidly urbanizing regions in the world, the national government issues overall policy on the pandemic. However, implementation is often contingent on local governments. Many policies aim to stem the spread of infection by controlling people's mobility or regulating their daily activities. Urban agglomerations are a strategic site of investigation in this light, because they consist of interconnected communities governed by various levels and jurisdictions. This paper analyzes the effects of policy interventions relative to confirmed cases in the seven major urban agglomerations in Indonesia (totaling 30 municipal/district governments). Data were collected from confirmed and fatality trends from March to mid-October 2020, which were contrasted with corresponding policies for each jurisdiction. By sorting the indicators of the spread of the pandemic and its corresponding control measures, we reach conclusions about which dimensions served to curb or trigger the surge of COVID-19 clusters. The analysis unsurprisingly shows that within each agglomeration, the main cities continue to represent the highest number of confirmed cases despite variations between them. This study also highlights two key findings. First, the effectiveness of distancing measures depends considerably on the capacity of governments to implement restrictions. For example, budget limitations resulted in uneven implementation of national mandates by decentralized authority. Facilities and services at different locations also influence our understanding of disease transmission. Second, people's ability and willingness to engage with a policy regime is contingent upon personal values or economic constraints. The study shows that viewing the spatial distribution of COVID-19 at the scale of urban agglomerations helps to explain key aspects of transmission and policy, pointing to recommendations about pursuing certain protocols. Nevertheless, key challenges remain to meet the full potential of this analytical approach, due to relatively low levels of testing and inadequate data collection measures in Indonesia. With higher densities, urban agglomerations account for the fastest rates of COVID-19 transmission. In Indonesia, one of the most rapidly urbanizing regions in the world, the national government issues overall policy on the pandemic. However, implementation is often contingent on local governments. Many policies aim to stem the spread of infection by controlling people's mobility or regulating their daily activities. Urban agglomerations are a strategic site of investigation in this light, because they consist of interconnected communities governed by various levels and jurisdictions. This paper analyzes the effects of policy interventions relative to confirmed cases in the seven major urban agglomerations in Indonesia (totaling 30 municipal/district governments). Data were collected from confirmed and fatality trends from March to mid-October 2020, which were contrasted with corresponding policies for each jurisdiction. By sorting the indicators of the spread of the pandemic and its corresponding control measures, we reach conclusions about which dimensions served to curb or trigger the surge of COVID-19 clusters. The analysis unsurprisingly shows that within each agglomeration, the main cities continue to represent the highest number of confirmed cases despite variations between them. This study also highlights two key findings. First, the effectiveness of distancing measures depends considerably on the capacity of governments to implement restrictions. For example, budget limitations resulted in uneven implementation of national mandates by decentralized authority. Facilities and services at different locations also influence our understanding of disease transmission. Second, people's ability and willingness to engage with a policy regime is contingent upon personal values or economic constraints. The study shows that viewing the spatial distribution of COVID-19 at the scale of urban agglomerations helps to explain key aspects of transmission and policy, pointing to recommendations about pursuing certain protocols. Nevertheless, key challenges remain to meet the full potential of this analytical approach, due to relatively low levels of testing and inadequate data collection measures in Indonesia.With higher densities, urban agglomerations account for the fastest rates of COVID-19 transmission. In Indonesia, one of the most rapidly urbanizing regions in the world, the national government issues overall policy on the pandemic. However, implementation is often contingent on local governments. Many policies aim to stem the spread of infection by controlling people's mobility or regulating their daily activities. Urban agglomerations are a strategic site of investigation in this light, because they consist of interconnected communities governed by various levels and jurisdictions. This paper analyzes the effects of policy interventions relative to confirmed cases in the seven major urban agglomerations in Indonesia (totaling 30 municipal/district governments). Data were collected from confirmed and fatality trends from March to mid-October 2020, which were contrasted with corresponding policies for each jurisdiction. By sorting the indicators of the spread of the pandemic and its corresponding control measures, we reach conclusions about which dimensions served to curb or trigger the surge of COVID-19 clusters. The analysis unsurprisingly shows that within each agglomeration, the main cities continue to represent the highest number of confirmed cases despite variations between them. This study also highlights two key findings. First, the effectiveness of distancing measures depends considerably on the capacity of governments to implement restrictions. For example, budget limitations resulted in uneven implementation of national mandates by decentralized authority. Facilities and services at different locations also influence our understanding of disease transmission. Second, people's ability and willingness to engage with a policy regime is contingent upon personal values or economic constraints. The study shows that viewing the spatial distribution of COVID-19 at the scale of urban agglomerations helps to explain key aspects of transmission and policy, pointing to recommendations about pursuing certain protocols. Nevertheless, key challenges remain to meet the full potential of this analytical approach, due to relatively low levels of testing and inadequate data collection measures in Indonesia. |
| ArticleNumber | 103015 |
| Author | Handayani, Wiwandari Fisher, Micah Mardhotillah, Santi Adam, Urban El-fatih Insani, Tia Dianing Gim, Tae-Hyoung Tommy |
| Author_xml | – sequence: 1 givenname: Wiwandari surname: Handayani fullname: Handayani, Wiwandari email: wiwandari.handayani@pwk.undip.ac.id organization: Diponegoro University, Semarang, 50275, Indonesia – sequence: 2 givenname: Tia Dianing orcidid: 0000-0002-5721-817X surname: Insani fullname: Insani, Tia Dianing organization: Diponegoro University, Semarang, 50275, Indonesia – sequence: 3 givenname: Micah surname: Fisher fullname: Fisher, Micah organization: East-West Center, Honolulu, United States – sequence: 4 givenname: Tae-Hyoung Tommy orcidid: 0000-0002-6623-1042 surname: Gim fullname: Gim, Tae-Hyoung Tommy organization: Seoul National University, Seoul, 08826, South Korea – sequence: 5 givenname: Santi surname: Mardhotillah fullname: Mardhotillah, Santi organization: Diponegoro University, Semarang, 50275, Indonesia – sequence: 6 givenname: Urban El-fatih orcidid: 0000-0002-1627-4095 surname: Adam fullname: Adam, Urban El-fatih organization: Hasanuddin University, 90245, Indonesia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35601393$$D View this record in MEDLINE/PubMed |
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| Copyright | 2022 Elsevier Ltd 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd |
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| Keywords | COVID-19 policy analysis Urban agglomerations Indonesia |
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| License | 2022 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
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