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Spatial drivers of COVID-19 vulnerability in Nigeria

INTRODUCTION: the spread and diffusion of COVID-19 undoubtedly shows strong spatial connotations and alignment with the physical indices of civilization and globalization. Several spatial risk factors have possible influence on its dispersal trajectory. Understanding their influence is critical for...

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Autores principales: Fasona, Mayowa Johnson, Okolie, Chukwuma John, Otitoloju, Adebayo Akeem
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The African Field Epidemiology Network 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8348361/
https://www.ncbi.nlm.nih.gov/pubmed/34394810
http://dx.doi.org/10.11604/pamj.2021.39.19.25791
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author Fasona, Mayowa Johnson
Okolie, Chukwuma John
Otitoloju, Adebayo Akeem
author_facet Fasona, Mayowa Johnson
Okolie, Chukwuma John
Otitoloju, Adebayo Akeem
author_sort Fasona, Mayowa Johnson
collection PubMed
description INTRODUCTION: the spread and diffusion of COVID-19 undoubtedly shows strong spatial connotations and alignment with the physical indices of civilization and globalization. Several spatial risk factors have possible influence on its dispersal trajectory. Understanding their influence is critical for mobilization, sensitization and managing non-pharmaceutical interventions at the appropriate spatial-administrative units. METHODS: on 01 April 2020, we constructed a rapid spatial diagnostics and generated vulnerability map for COVID-19 infection spread at state level using 12 core spatial drivers. The risk factors used include established COVID-19 cases (as at 01 April 2020), population, proximity to the airports, inter-state road traffic, intra-state road traffic, intra city traffic, international road traffic, possible influx of elites from abroad, preponderance of high risk political elite, likelihood of religious gathering, likelihood of other social gatherings, and proximity to existing COVID-19 test centers. These were also tested as predictors of COVID-19 spread using multiple regression analysis. RESULTS: the results show that 6 States - Lagos, Kano, Katsina, Kaduna, Oyo and Rivers - and the Federal Capital Territory have very high vulnerability, 17 states have high vulnerability and 13 states have medium vulnerability to COVID-19 transmission. Several drivers show a strong association with COVID-19 with the coefficient of correlation ranging from 0.983 - 0.995. The regression analysis indicates that between 96.6 and 99.0 percent of the total variation in the COVID-19 infections across Nigeria can be explained by the predictors. CONCLUSION: the spatial pattern of infection across the states are substantially consistent with the predicted pattern of vulnerability.
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spelling pubmed-83483612021-08-13 Spatial drivers of COVID-19 vulnerability in Nigeria Fasona, Mayowa Johnson Okolie, Chukwuma John Otitoloju, Adebayo Akeem Pan Afr Med J Research INTRODUCTION: the spread and diffusion of COVID-19 undoubtedly shows strong spatial connotations and alignment with the physical indices of civilization and globalization. Several spatial risk factors have possible influence on its dispersal trajectory. Understanding their influence is critical for mobilization, sensitization and managing non-pharmaceutical interventions at the appropriate spatial-administrative units. METHODS: on 01 April 2020, we constructed a rapid spatial diagnostics and generated vulnerability map for COVID-19 infection spread at state level using 12 core spatial drivers. The risk factors used include established COVID-19 cases (as at 01 April 2020), population, proximity to the airports, inter-state road traffic, intra-state road traffic, intra city traffic, international road traffic, possible influx of elites from abroad, preponderance of high risk political elite, likelihood of religious gathering, likelihood of other social gatherings, and proximity to existing COVID-19 test centers. These were also tested as predictors of COVID-19 spread using multiple regression analysis. RESULTS: the results show that 6 States - Lagos, Kano, Katsina, Kaduna, Oyo and Rivers - and the Federal Capital Territory have very high vulnerability, 17 states have high vulnerability and 13 states have medium vulnerability to COVID-19 transmission. Several drivers show a strong association with COVID-19 with the coefficient of correlation ranging from 0.983 - 0.995. The regression analysis indicates that between 96.6 and 99.0 percent of the total variation in the COVID-19 infections across Nigeria can be explained by the predictors. CONCLUSION: the spatial pattern of infection across the states are substantially consistent with the predicted pattern of vulnerability. The African Field Epidemiology Network 2021-05-07 /pmc/articles/PMC8348361/ /pubmed/34394810 http://dx.doi.org/10.11604/pamj.2021.39.19.25791 Text en Copyright: Mayowa Johnson Fasona et al. https://creativecommons.org/licenses/by/4.0/The Pan African Medical Journal (ISSN: 1937-8688). This is an Open Access article distributed under the terms of the Creative Commons Attribution International 4.0 License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Fasona, Mayowa Johnson
Okolie, Chukwuma John
Otitoloju, Adebayo Akeem
Spatial drivers of COVID-19 vulnerability in Nigeria
title Spatial drivers of COVID-19 vulnerability in Nigeria
title_full Spatial drivers of COVID-19 vulnerability in Nigeria
title_fullStr Spatial drivers of COVID-19 vulnerability in Nigeria
title_full_unstemmed Spatial drivers of COVID-19 vulnerability in Nigeria
title_short Spatial drivers of COVID-19 vulnerability in Nigeria
title_sort spatial drivers of covid-19 vulnerability in nigeria
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8348361/
https://www.ncbi.nlm.nih.gov/pubmed/34394810
http://dx.doi.org/10.11604/pamj.2021.39.19.25791
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