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Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria

The reemergence of monkeypoxvirus (MPXV) in 2017 after about 39 years of no reported cases in Nigeria, and the recent incidence in countries such as the United States of America, United Kingdom, Singapore, and Israel which have been reportedly linked with travelers from Africa, have heightened conce...

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Autores principales: Arotolu, Temitope Emmanuel, Afe, Ayoola Ebenezer, Wang, HaoNing, Lv, JiaNing, Shi, Kun, Huang, LiYa, Wang, XiaoLong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9488772/
https://www.ncbi.nlm.nih.gov/pubmed/36126054
http://dx.doi.org/10.1371/journal.pone.0274325
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author Arotolu, Temitope Emmanuel
Afe, Ayoola Ebenezer
Wang, HaoNing
Lv, JiaNing
Shi, Kun
Huang, LiYa
Wang, XiaoLong
author_facet Arotolu, Temitope Emmanuel
Afe, Ayoola Ebenezer
Wang, HaoNing
Lv, JiaNing
Shi, Kun
Huang, LiYa
Wang, XiaoLong
author_sort Arotolu, Temitope Emmanuel
collection PubMed
description The reemergence of monkeypoxvirus (MPXV) in 2017 after about 39 years of no reported cases in Nigeria, and the recent incidence in countries such as the United States of America, United Kingdom, Singapore, and Israel which have been reportedly linked with travelers from Africa, have heightened concern that MPXV may have emerged to occupy the vacant ecological and immunological niche created by the extinct smallpox virus. This study was carried out to identify environmental conditions and areas that are environmentally suitable (risky areas) for MPXV in southern Nigeria. One hundred and sixteen (116) spatially unique MPXV occurrence data from 2017–2021 and corresponding environmental variables were spatially modeled by a maximum entropy algorithm to evaluate the contribution of the variables to the distribution of the viral disease. A variance inflation analysis was adopted to limit the number of environmental variables and minimize multicollinearity. The five variables that contributed to the suitability model for MPXV disease are precipitation of driest quarter (47%), elevation (26%), human population density (17%), minimum temperature in December (7%), and maximum temperature in March (3%). For validation, our model had a high AUC value of 0.92 and standard deviation of 0.009 indicating that it had excellent ability to predict the suitable areas for monkeypox disease. Categorized risk classes across southern states was also identified. A total of eight states were predicted to be at high risk of monkeypox outbreak occurrence. These findings can guide policymakers in resources allocation and distribution to effectively implement targeted control measures for MPXV outbreaks in southern Nigeria.
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spelling pubmed-94887722022-09-21 Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria Arotolu, Temitope Emmanuel Afe, Ayoola Ebenezer Wang, HaoNing Lv, JiaNing Shi, Kun Huang, LiYa Wang, XiaoLong PLoS One Research Article The reemergence of monkeypoxvirus (MPXV) in 2017 after about 39 years of no reported cases in Nigeria, and the recent incidence in countries such as the United States of America, United Kingdom, Singapore, and Israel which have been reportedly linked with travelers from Africa, have heightened concern that MPXV may have emerged to occupy the vacant ecological and immunological niche created by the extinct smallpox virus. This study was carried out to identify environmental conditions and areas that are environmentally suitable (risky areas) for MPXV in southern Nigeria. One hundred and sixteen (116) spatially unique MPXV occurrence data from 2017–2021 and corresponding environmental variables were spatially modeled by a maximum entropy algorithm to evaluate the contribution of the variables to the distribution of the viral disease. A variance inflation analysis was adopted to limit the number of environmental variables and minimize multicollinearity. The five variables that contributed to the suitability model for MPXV disease are precipitation of driest quarter (47%), elevation (26%), human population density (17%), minimum temperature in December (7%), and maximum temperature in March (3%). For validation, our model had a high AUC value of 0.92 and standard deviation of 0.009 indicating that it had excellent ability to predict the suitable areas for monkeypox disease. Categorized risk classes across southern states was also identified. A total of eight states were predicted to be at high risk of monkeypox outbreak occurrence. These findings can guide policymakers in resources allocation and distribution to effectively implement targeted control measures for MPXV outbreaks in southern Nigeria. Public Library of Science 2022-09-20 /pmc/articles/PMC9488772/ /pubmed/36126054 http://dx.doi.org/10.1371/journal.pone.0274325 Text en © 2022 Arotolu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Arotolu, Temitope Emmanuel
Afe, Ayoola Ebenezer
Wang, HaoNing
Lv, JiaNing
Shi, Kun
Huang, LiYa
Wang, XiaoLong
Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria
title Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria
title_full Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria
title_fullStr Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria
title_full_unstemmed Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria
title_short Spatial modeling and ecological suitability of monkeypox disease in Southern Nigeria
title_sort spatial modeling and ecological suitability of monkeypox disease in southern nigeria
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9488772/
https://www.ncbi.nlm.nih.gov/pubmed/36126054
http://dx.doi.org/10.1371/journal.pone.0274325
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