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Factors shaping the COVID-19 epidemic curve: a multi-country analysis
BACKGROUND: Lockdown measures are the backbone of containment measures for the COVID-19 pandemic both in high-income countries (HICs) and low- and middle-income countries (LMICs). However, in view of the inevitably-occurring second and third global covid-19 wave, assessing the success and impact of...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487341/ https://www.ncbi.nlm.nih.gov/pubmed/34600485 http://dx.doi.org/10.1186/s12879-021-06714-3 |
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author | Jang, Su Yeon Hussain-Alkhateeb, Laith Rivera Ramirez, Tatiana Al-Aghbari, Ahmed Asa’ad Chackalackal, Dhia Joseph Cardenas-Sanchez, Rocio Carrillo, Maria Angelica Oh, In-Hwan Alfonso-Sierra, Eduardo Andrés Oechsner, Pia Kibiwott Kirui, Brian Anto, Martin Diaz-Monsalve, Sonia Kroeger, Axel |
author_facet | Jang, Su Yeon Hussain-Alkhateeb, Laith Rivera Ramirez, Tatiana Al-Aghbari, Ahmed Asa’ad Chackalackal, Dhia Joseph Cardenas-Sanchez, Rocio Carrillo, Maria Angelica Oh, In-Hwan Alfonso-Sierra, Eduardo Andrés Oechsner, Pia Kibiwott Kirui, Brian Anto, Martin Diaz-Monsalve, Sonia Kroeger, Axel |
author_sort | Jang, Su Yeon |
collection | PubMed |
description | BACKGROUND: Lockdown measures are the backbone of containment measures for the COVID-19 pandemic both in high-income countries (HICs) and low- and middle-income countries (LMICs). However, in view of the inevitably-occurring second and third global covid-19 wave, assessing the success and impact of containment measures on the epidemic curve of COVID-19 and people’s compliance with such measures is crucial for more effective policies. To determine the containment measures influencing the COVID-19 epidemic curve in nine targeted countries across high-, middle-, and low-income nations. METHODS: Four HICs (Germany, Sweden, Italy, and South Korea) and five LMICs (Mexico, Colombia, India, Nigeria, and Nepal) were selected to assess the association using interrupted time series analysis of daily case numbers and deaths of COVID-19 considering the following factors: The “stringency index (SI)” indicating how tight the containment measures were implemented in each country; and the level of compliance with the prescribed measures using human mobility data. Additionally, a scoping review was conducted to contextualize the findings. RESULTS: Most countries implemented quite rigorous lockdown measures, particularly the LMICs (India, Nepal, and Colombia) following the model of HICs (Germany and Italy). Exceptions were Sweden and South Korea, which opted for different strategies. The compliance with the restrictions—measured as mobility related to home office, restraining from leisure activities, non-use of local transport and others—was generally good, except in Sweden and South Korea where the restrictions were limited. The endemic curves and time-series analysis showed that the containment measures were successful in HICs but not in LMICs. CONCLUSION: The imposed lockdown measures are alarming, particularly in resource-constrained settings where such measures are independent of the population segment, which drives the virus transmission. Methods for examining people’s movements or hardships that are caused by covid- no work, no food situation are inequitable. Novel and context-adapted approach of dealing with the COVID-19 crisis are therefore crucial. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06714-3. |
format | Online Article Text |
id | pubmed-8487341 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-84873412021-10-04 Factors shaping the COVID-19 epidemic curve: a multi-country analysis Jang, Su Yeon Hussain-Alkhateeb, Laith Rivera Ramirez, Tatiana Al-Aghbari, Ahmed Asa’ad Chackalackal, Dhia Joseph Cardenas-Sanchez, Rocio Carrillo, Maria Angelica Oh, In-Hwan Alfonso-Sierra, Eduardo Andrés Oechsner, Pia Kibiwott Kirui, Brian Anto, Martin Diaz-Monsalve, Sonia Kroeger, Axel BMC Infect Dis Research Article BACKGROUND: Lockdown measures are the backbone of containment measures for the COVID-19 pandemic both in high-income countries (HICs) and low- and middle-income countries (LMICs). However, in view of the inevitably-occurring second and third global covid-19 wave, assessing the success and impact of containment measures on the epidemic curve of COVID-19 and people’s compliance with such measures is crucial for more effective policies. To determine the containment measures influencing the COVID-19 epidemic curve in nine targeted countries across high-, middle-, and low-income nations. METHODS: Four HICs (Germany, Sweden, Italy, and South Korea) and five LMICs (Mexico, Colombia, India, Nigeria, and Nepal) were selected to assess the association using interrupted time series analysis of daily case numbers and deaths of COVID-19 considering the following factors: The “stringency index (SI)” indicating how tight the containment measures were implemented in each country; and the level of compliance with the prescribed measures using human mobility data. Additionally, a scoping review was conducted to contextualize the findings. RESULTS: Most countries implemented quite rigorous lockdown measures, particularly the LMICs (India, Nepal, and Colombia) following the model of HICs (Germany and Italy). Exceptions were Sweden and South Korea, which opted for different strategies. The compliance with the restrictions—measured as mobility related to home office, restraining from leisure activities, non-use of local transport and others—was generally good, except in Sweden and South Korea where the restrictions were limited. The endemic curves and time-series analysis showed that the containment measures were successful in HICs but not in LMICs. CONCLUSION: The imposed lockdown measures are alarming, particularly in resource-constrained settings where such measures are independent of the population segment, which drives the virus transmission. Methods for examining people’s movements or hardships that are caused by covid- no work, no food situation are inequitable. Novel and context-adapted approach of dealing with the COVID-19 crisis are therefore crucial. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-021-06714-3. BioMed Central 2021-10-02 /pmc/articles/PMC8487341/ /pubmed/34600485 http://dx.doi.org/10.1186/s12879-021-06714-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Jang, Su Yeon Hussain-Alkhateeb, Laith Rivera Ramirez, Tatiana Al-Aghbari, Ahmed Asa’ad Chackalackal, Dhia Joseph Cardenas-Sanchez, Rocio Carrillo, Maria Angelica Oh, In-Hwan Alfonso-Sierra, Eduardo Andrés Oechsner, Pia Kibiwott Kirui, Brian Anto, Martin Diaz-Monsalve, Sonia Kroeger, Axel Factors shaping the COVID-19 epidemic curve: a multi-country analysis |
title | Factors shaping the COVID-19 epidemic curve: a multi-country analysis |
title_full | Factors shaping the COVID-19 epidemic curve: a multi-country analysis |
title_fullStr | Factors shaping the COVID-19 epidemic curve: a multi-country analysis |
title_full_unstemmed | Factors shaping the COVID-19 epidemic curve: a multi-country analysis |
title_short | Factors shaping the COVID-19 epidemic curve: a multi-country analysis |
title_sort | factors shaping the covid-19 epidemic curve: a multi-country analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8487341/ https://www.ncbi.nlm.nih.gov/pubmed/34600485 http://dx.doi.org/10.1186/s12879-021-06714-3 |
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