The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/48487 |
Resumo: | The world is facing the coronavirus pandemic (COVID-19), which began in China. By August 18, 2020, the United States, Brazil, and India were the most affected countries. Health infrastructure and socioeconomic vulnerabilities may be affecting the response capacities of these countries. We compared official indicators to identify which vulnerabilities better determined the exposure risk to COVID-19 in both the most and least affected countries. To achieve this purpose, we collected indicators from the Infectious Disease Vulnerability Index (IDVI), the World Health Organization (WHO), the World Bank, and the Brazilian Geography and Statistics Institute (IBGE). All indicators were normalized to facilitate comparisons. Speed, incidence, and population were used to identify the groups of countries with the highest and lowest risks of infection. Countries’ response capacities were determined based on socioeconomic, political, and health infrastructure conditions. Vulnerabilities were identified based on the indicator sensitivity. The highest-risk group included the U.S., Brazil, and India, whereas the lowest-risk group (with the largest population by continent) consisted of China, New Zealand, and Germany. The high-sensitivity cluster had 18 indicators (50% extra IDVI), such as merchandise trade, immunization, public services, maternal mortality, life expectancy at birth, hospital beds, GINI index, adolescent fertility, governance, political stability, transparency/corruption, industry, and water supply. The greatest vulnerability of the highest-risk group was related first to economic factors (merchandise trade), followed by public health (immunization), highlighting global dependence on Chinese trade, such as protective materials, equipment, and diagnostic tests. However, domestic political factors had more indicators, beginning with high sensitivity and followed by healthcare and economic conditions, which signified a lesser capacity to guide, coordinate, and supply the population with protective measures, such as social distancing. |
id |
UFLA_4819d3682911724d6ecf803f05edd498 |
---|---|
oai_identifier_str |
oai:localhost:1/48487 |
network_acronym_str |
UFLA |
network_name_str |
Repositório Institucional da UFLA |
repository_id_str |
|
spelling |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment modelCOVID-19CoronavirusPandemicsMedical risk factorsPandemiasFatores de risco médicoThe world is facing the coronavirus pandemic (COVID-19), which began in China. By August 18, 2020, the United States, Brazil, and India were the most affected countries. Health infrastructure and socioeconomic vulnerabilities may be affecting the response capacities of these countries. We compared official indicators to identify which vulnerabilities better determined the exposure risk to COVID-19 in both the most and least affected countries. To achieve this purpose, we collected indicators from the Infectious Disease Vulnerability Index (IDVI), the World Health Organization (WHO), the World Bank, and the Brazilian Geography and Statistics Institute (IBGE). All indicators were normalized to facilitate comparisons. Speed, incidence, and population were used to identify the groups of countries with the highest and lowest risks of infection. Countries’ response capacities were determined based on socioeconomic, political, and health infrastructure conditions. Vulnerabilities were identified based on the indicator sensitivity. The highest-risk group included the U.S., Brazil, and India, whereas the lowest-risk group (with the largest population by continent) consisted of China, New Zealand, and Germany. The high-sensitivity cluster had 18 indicators (50% extra IDVI), such as merchandise trade, immunization, public services, maternal mortality, life expectancy at birth, hospital beds, GINI index, adolescent fertility, governance, political stability, transparency/corruption, industry, and water supply. The greatest vulnerability of the highest-risk group was related first to economic factors (merchandise trade), followed by public health (immunization), highlighting global dependence on Chinese trade, such as protective materials, equipment, and diagnostic tests. However, domestic political factors had more indicators, beginning with high sensitivity and followed by healthcare and economic conditions, which signified a lesser capacity to guide, coordinate, and supply the population with protective measures, such as social distancing.PLoS ONE2021-11-17T17:05:12Z2021-11-17T17:05:12Z2021info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfCARTAXO, A. N. S. et al. The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model. PLoS ONE, [S. l.], v. 16, n. 3, e0248075, 2021. DOI: 10.1371/journal.pone.0248075.http://repositorio.ufla.br/jspui/handle/1/48487PLoS ONEreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessCartaxo, Adriana Nascimento SantosBarbosa, Francisco Iran CartaxoBermejo, Paulo Henrique de SouzaMoreira, Marina FigueiredoPrata, David Nadlereng2023-05-03T13:18:04Zoai:localhost:1/48487Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T13:18:04Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model |
title |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model |
spellingShingle |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model Cartaxo, Adriana Nascimento Santos COVID-19 Coronavirus Pandemics Medical risk factors Pandemias Fatores de risco médico |
title_short |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model |
title_full |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model |
title_fullStr |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model |
title_full_unstemmed |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model |
title_sort |
The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model |
author |
Cartaxo, Adriana Nascimento Santos |
author_facet |
Cartaxo, Adriana Nascimento Santos Barbosa, Francisco Iran Cartaxo Bermejo, Paulo Henrique de Souza Moreira, Marina Figueiredo Prata, David Nadler |
author_role |
author |
author2 |
Barbosa, Francisco Iran Cartaxo Bermejo, Paulo Henrique de Souza Moreira, Marina Figueiredo Prata, David Nadler |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Cartaxo, Adriana Nascimento Santos Barbosa, Francisco Iran Cartaxo Bermejo, Paulo Henrique de Souza Moreira, Marina Figueiredo Prata, David Nadler |
dc.subject.por.fl_str_mv |
COVID-19 Coronavirus Pandemics Medical risk factors Pandemias Fatores de risco médico |
topic |
COVID-19 Coronavirus Pandemics Medical risk factors Pandemias Fatores de risco médico |
description |
The world is facing the coronavirus pandemic (COVID-19), which began in China. By August 18, 2020, the United States, Brazil, and India were the most affected countries. Health infrastructure and socioeconomic vulnerabilities may be affecting the response capacities of these countries. We compared official indicators to identify which vulnerabilities better determined the exposure risk to COVID-19 in both the most and least affected countries. To achieve this purpose, we collected indicators from the Infectious Disease Vulnerability Index (IDVI), the World Health Organization (WHO), the World Bank, and the Brazilian Geography and Statistics Institute (IBGE). All indicators were normalized to facilitate comparisons. Speed, incidence, and population were used to identify the groups of countries with the highest and lowest risks of infection. Countries’ response capacities were determined based on socioeconomic, political, and health infrastructure conditions. Vulnerabilities were identified based on the indicator sensitivity. The highest-risk group included the U.S., Brazil, and India, whereas the lowest-risk group (with the largest population by continent) consisted of China, New Zealand, and Germany. The high-sensitivity cluster had 18 indicators (50% extra IDVI), such as merchandise trade, immunization, public services, maternal mortality, life expectancy at birth, hospital beds, GINI index, adolescent fertility, governance, political stability, transparency/corruption, industry, and water supply. The greatest vulnerability of the highest-risk group was related first to economic factors (merchandise trade), followed by public health (immunization), highlighting global dependence on Chinese trade, such as protective materials, equipment, and diagnostic tests. However, domestic political factors had more indicators, beginning with high sensitivity and followed by healthcare and economic conditions, which signified a lesser capacity to guide, coordinate, and supply the population with protective measures, such as social distancing. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-11-17T17:05:12Z 2021-11-17T17:05:12Z 2021 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
CARTAXO, A. N. S. et al. The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model. PLoS ONE, [S. l.], v. 16, n. 3, e0248075, 2021. DOI: 10.1371/journal.pone.0248075. http://repositorio.ufla.br/jspui/handle/1/48487 |
identifier_str_mv |
CARTAXO, A. N. S. et al. The exposure risk to COVID-19 in most affected countries: a vulnerability assessment model. PLoS ONE, [S. l.], v. 16, n. 3, e0248075, 2021. DOI: 10.1371/journal.pone.0248075. |
url |
http://repositorio.ufla.br/jspui/handle/1/48487 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution 4.0 International http://creativecommons.org/licenses/by/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
PLoS ONE |
publisher.none.fl_str_mv |
PLoS ONE |
dc.source.none.fl_str_mv |
PLoS ONE reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1815439333759385600 |