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Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060
African horse sickness is a vector-borne, non-contagious and highly infectious disease of equines caused by African horse sickness viruses (AHSv) that mainly affect horses. The occurrence of the disease causes huge economic impacts because of its high fatality rate, trade ban and disease control cos...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811056/ https://www.ncbi.nlm.nih.gov/pubmed/35110661 http://dx.doi.org/10.1038/s41598-022-05826-3 |
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author | Assefa, Ayalew Tibebu, Abebe Bihon, Amare Dagnachew, Alemu Muktar, Yimer |
author_facet | Assefa, Ayalew Tibebu, Abebe Bihon, Amare Dagnachew, Alemu Muktar, Yimer |
author_sort | Assefa, Ayalew |
collection | PubMed |
description | African horse sickness is a vector-borne, non-contagious and highly infectious disease of equines caused by African horse sickness viruses (AHSv) that mainly affect horses. The occurrence of the disease causes huge economic impacts because of its high fatality rate, trade ban and disease control costs. In the planning of vectors and vector-borne diseases like AHS, the application of Ecological niche models (ENM) used an enormous contribution in precisely delineating the suitable habitats of the vector. We developed an ENM to delineate the global suitability of AHSv based on retrospective outbreak data records from 2005 to 2019. The model was developed in an R software program using the Biomod2 package with an Ensemble modeling technique. Predictive environmental variables like mean diurnal range, mean precipitation of driest month(mm), precipitation seasonality (cv), mean annual maximum temperature ((o)c), mean annual minimum temperature ((o)c), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), mean annual precipitation (mm), solar radiation (kj /day), elevation/altitude (m), wind speed (m/s) were used to develop the model. From these variables, solar radiation, mean maximum temperature, average annual precipitation, altitude and precipitation seasonality contributed 36.83%, 17.1%, 14.34%, 7.61%, and 6.4%, respectively. The model depicted the sub-Sahara African continent as the most suitable area for the virus. Mainly Senegal, Burkina Faso, Niger, Nigeria, Ethiopia, Sudan, Somalia, South Africa, Zimbabwe, Madagascar and Malawi are African countries identified as highly suitable countries for the virus. Besides, OIE-listed disease-free countries like India, Australia, Brazil, Paraguay and Bolivia have been found suitable for the virus. This model can be used as an epidemiological tool in planning control and surveillance of diseases nationally or internationally. |
format | Online Article Text |
id | pubmed-8811056 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-88110562022-02-07 Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060 Assefa, Ayalew Tibebu, Abebe Bihon, Amare Dagnachew, Alemu Muktar, Yimer Sci Rep Article African horse sickness is a vector-borne, non-contagious and highly infectious disease of equines caused by African horse sickness viruses (AHSv) that mainly affect horses. The occurrence of the disease causes huge economic impacts because of its high fatality rate, trade ban and disease control costs. In the planning of vectors and vector-borne diseases like AHS, the application of Ecological niche models (ENM) used an enormous contribution in precisely delineating the suitable habitats of the vector. We developed an ENM to delineate the global suitability of AHSv based on retrospective outbreak data records from 2005 to 2019. The model was developed in an R software program using the Biomod2 package with an Ensemble modeling technique. Predictive environmental variables like mean diurnal range, mean precipitation of driest month(mm), precipitation seasonality (cv), mean annual maximum temperature ((o)c), mean annual minimum temperature ((o)c), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), mean annual precipitation (mm), solar radiation (kj /day), elevation/altitude (m), wind speed (m/s) were used to develop the model. From these variables, solar radiation, mean maximum temperature, average annual precipitation, altitude and precipitation seasonality contributed 36.83%, 17.1%, 14.34%, 7.61%, and 6.4%, respectively. The model depicted the sub-Sahara African continent as the most suitable area for the virus. Mainly Senegal, Burkina Faso, Niger, Nigeria, Ethiopia, Sudan, Somalia, South Africa, Zimbabwe, Madagascar and Malawi are African countries identified as highly suitable countries for the virus. Besides, OIE-listed disease-free countries like India, Australia, Brazil, Paraguay and Bolivia have been found suitable for the virus. This model can be used as an epidemiological tool in planning control and surveillance of diseases nationally or internationally. Nature Publishing Group UK 2022-02-02 /pmc/articles/PMC8811056/ /pubmed/35110661 http://dx.doi.org/10.1038/s41598-022-05826-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This 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/) . |
spellingShingle | Article Assefa, Ayalew Tibebu, Abebe Bihon, Amare Dagnachew, Alemu Muktar, Yimer Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060 |
title | Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060 |
title_full | Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060 |
title_fullStr | Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060 |
title_full_unstemmed | Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060 |
title_short | Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060 |
title_sort | ecological niche modeling predicting the potential distribution of african horse sickness virus from 2020 to 2060 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8811056/ https://www.ncbi.nlm.nih.gov/pubmed/35110661 http://dx.doi.org/10.1038/s41598-022-05826-3 |
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