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Spatial Analysis of Global Variability in Covid-19 Burden

BACKGROUND: Since the first occurrence of coronavirus disease 2019 (Covid-19), a number of online tools have become available to assist with tracking Covid-19 prevalence. Yet we are unaware of resources that provide country-specific Covid-19 incidence data. METHODS: We undertook a descriptive analys...

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Autores principales: Miller, Larry E, Bhattacharyya, Ruemon, Miller, Anna L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280244/
https://www.ncbi.nlm.nih.gov/pubmed/32581614
http://dx.doi.org/10.2147/RMHP.S255793
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author Miller, Larry E
Bhattacharyya, Ruemon
Miller, Anna L
author_facet Miller, Larry E
Bhattacharyya, Ruemon
Miller, Anna L
author_sort Miller, Larry E
collection PubMed
description BACKGROUND: Since the first occurrence of coronavirus disease 2019 (Covid-19), a number of online tools have become available to assist with tracking Covid-19 prevalence. Yet we are unaware of resources that provide country-specific Covid-19 incidence data. METHODS: We undertook a descriptive analysis of the global impact of Covid-19 using data reported on March 17, 2020. The prevalence of Covid-19 cases, fatalities attributed to Covid-19, and the case fatality rate for each of the 238 countries were accessed from the World Health Organization global Covid-19 tracking site, and we additionally calculated Covid-19 incidence based on country-specific population data. We determined the country-specific point prevalence and incidence of Covid-19 and associated deaths while using geocoded data to display their spatial distribution with geographic heat maps. RESULTS: The analysis included 193,197 Covid-19 cases and 7859 associated deaths. The point prevalence was highest in China (80,881), Italy (31,506), Iran (16,169), and Spain (11,312); no other country reported more than 10,000 cases. The incidence (per million population) was highest in San Marino (3389) followed by Iceland (645) and Italy (521); no other country had an incidence above 400 per million population. CONCLUSION: Countries with a high Covid-19 prevalence may not have a high incidence, and vice versa. Public health agencies that provide real-time infection tracking tools should report country-specific Covid-19 incidence metrics, in addition to prevalence data.
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spelling pubmed-72802442020-06-23 Spatial Analysis of Global Variability in Covid-19 Burden Miller, Larry E Bhattacharyya, Ruemon Miller, Anna L Risk Manag Healthc Policy Rapid Communication BACKGROUND: Since the first occurrence of coronavirus disease 2019 (Covid-19), a number of online tools have become available to assist with tracking Covid-19 prevalence. Yet we are unaware of resources that provide country-specific Covid-19 incidence data. METHODS: We undertook a descriptive analysis of the global impact of Covid-19 using data reported on March 17, 2020. The prevalence of Covid-19 cases, fatalities attributed to Covid-19, and the case fatality rate for each of the 238 countries were accessed from the World Health Organization global Covid-19 tracking site, and we additionally calculated Covid-19 incidence based on country-specific population data. We determined the country-specific point prevalence and incidence of Covid-19 and associated deaths while using geocoded data to display their spatial distribution with geographic heat maps. RESULTS: The analysis included 193,197 Covid-19 cases and 7859 associated deaths. The point prevalence was highest in China (80,881), Italy (31,506), Iran (16,169), and Spain (11,312); no other country reported more than 10,000 cases. The incidence (per million population) was highest in San Marino (3389) followed by Iceland (645) and Italy (521); no other country had an incidence above 400 per million population. CONCLUSION: Countries with a high Covid-19 prevalence may not have a high incidence, and vice versa. Public health agencies that provide real-time infection tracking tools should report country-specific Covid-19 incidence metrics, in addition to prevalence data. Dove 2020-06-04 /pmc/articles/PMC7280244/ /pubmed/32581614 http://dx.doi.org/10.2147/RMHP.S255793 Text en © 2020 Miller et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Rapid Communication
Miller, Larry E
Bhattacharyya, Ruemon
Miller, Anna L
Spatial Analysis of Global Variability in Covid-19 Burden
title Spatial Analysis of Global Variability in Covid-19 Burden
title_full Spatial Analysis of Global Variability in Covid-19 Burden
title_fullStr Spatial Analysis of Global Variability in Covid-19 Burden
title_full_unstemmed Spatial Analysis of Global Variability in Covid-19 Burden
title_short Spatial Analysis of Global Variability in Covid-19 Burden
title_sort spatial analysis of global variability in covid-19 burden
topic Rapid Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7280244/
https://www.ncbi.nlm.nih.gov/pubmed/32581614
http://dx.doi.org/10.2147/RMHP.S255793
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