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Scientific reports2022; 12(1); 1748; doi: 10.1038/s41598-022-05826-3

Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060.

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.
© 2022. The Author(s).
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Publication Date: 2022-02-02 PubMed ID: 35110661PubMed Central: PMC8811056DOI: 10.1038/s41598-022-05826-3Google Scholar: Lookup
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  • Journal Article

Summary

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The research article presents a study where an Ecological Niche Model (ENM) was developed to predict the potential global distribution of African Horse Sickness Virus (AHSv) from 2020 to 2060, using past outbreak data from 2005 to 2019.

Methodology

  • The research team used an Ensemble modeling technique on the R software program using the Biomod2 package to generate the ENM.
  • This model was built using numerous predictive environmental variables, including mean diurnal range, mean precipitation of the driest month, precipitation seasonality, mean annual maximum temperature, mean annual minimum temperature, mean precipitation of the warmest quarter, mean precipitation of the coldest quarter, mean annual precipitation, solar radiation, elevation/altitude, and wind speed.
  • Amongst these, the most significant contributing factors to the model were solar radiation (contributing 36.83%), mean maximum temperature (17.1%), average annual precipitation (14.34%), altitude (7.61%), and precipitation seasonality (6.4%).

Findings

  • The model predicted that Sub-Saharan African countries, including Senegal, Burkina Faso, Niger, Nigeria, Ethiopia, Sudan, Somalia, South Africa, Zimbabwe, Madagascar, and Malawi, are highly suitable for AHSv.
  • The study also discovered that countries currently listed as disease free by the World Organisation for Animal Health (OIE), such as India, Australia, Brazil, Paraguay, and Bolivia, showed a suitable environment for the virus, thus suggesting future virus spread to these regions is possible.

Implications

  • The developed ENM proves to be a very useful tool in epidemiology for planning disease control and surveillance both nationally and internationally.
  • Understanding the potential spread of AHSv can help countries prepare better for outbreaks, even in previously disease-free areas, thus reducing the disease’s economic impact due to mortality, trade bans, and disease control costs.

Cite This Article

APA
Assefa A, Tibebu A, Bihon A, Dagnachew A, Muktar Y. (2022). Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060. Sci Rep, 12(1), 1748. https://doi.org/10.1038/s41598-022-05826-3

Publication

Scientific reports
ISSN: 2045-2322
NlmUniqueID: 101563288
Country: England
Language: English
Volume: 12
Issue: 1
Pages: 1748

Researcher Affiliations

Assefa, Ayalew
  • Department of Veterinary Medicine, Woldia University, Woldia, Ethiopia. hayall2020@gmail.com.
Tibebu, Abebe
  • Sekota Dryland Agricultural Research Center, Sekota, Ethiopia.
Bihon, Amare
  • Department of Veterinary Medicine, Woldia University, Woldia, Ethiopia.
Dagnachew, Alemu
  • Sekota Dryland Agricultural Research Center, Sekota, Ethiopia.
Muktar, Yimer
  • Department of Veterinary Medicine, Woldia University, Woldia, Ethiopia.

MeSH Terms

  • Africa / epidemiology
  • African Horse Sickness / epidemiology
  • African Horse Sickness / transmission
  • African Horse Sickness Virus
  • Animals
  • Ceratopogonidae / virology
  • Disease Outbreaks / veterinary
  • Ecosystem
  • Horses
  • India / epidemiology
  • Insect Vectors / virology
  • Models, Statistical
  • Software
  • South Africa / epidemiology
  • South America / epidemiology
  • Temperature
  • Vector Borne Diseases / epidemiology
  • Vector Borne Diseases / transmission
  • Vector Borne Diseases / veterinary

Conflict of Interest Statement

The authors declare no competing interests.

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