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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.
Afiliação
  • Assefa A; Department of Veterinary Medicine, Woldia University, Woldia, Ethiopia. hayall2020@gmail.com.
  • Tibebu A; Sekota Dryland Agricultural Research Center, Sekota, Ethiopia.
  • Bihon A; Department of Veterinary Medicine, Woldia University, Woldia, Ethiopia.
  • Dagnachew A; Sekota Dryland Agricultural Research Center, Sekota, Ethiopia.
  • Muktar Y; Department of Veterinary Medicine, Woldia University, Woldia, Ethiopia.
Sci Rep ; 12(1): 1748, 2022 02 02.
Article em En | MEDLINE | ID: mdl-35110661
ABSTRACT
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 (oc), mean annual minimum temperature (oc), 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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos / Ecossistema / Doença Equina Africana / Vírus da Doença Equina Africana Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals País/Região como assunto: Africa / America do sul / Asia Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Modelos Estatísticos / Ecossistema / Doença Equina Africana / Vírus da Doença Equina Africana Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Animals País/Região como assunto: Africa / America do sul / Asia Idioma: En Revista: Sci Rep Ano de publicação: 2022 Tipo de documento: Article