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.

Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China

“…However, due to multicollinearity, these variables could not all be included in the model. Multicollinearity checks were performed on 26 environmental variables which were verified using the VIF procedure of the USDM package in R software 20 . Variables with Pearson correlation coefficient greater than 0.7 and VIF values greater than 8 were excluded from the variable set.…”

“…CA has been incorporated in the ensemble modeling approach. First, it could be used to predict a suitable niche model, and second, it could also be used to evaluate the performance of the model 20 .…”

Culicoides-borne Orbivirus epidemiology in a changing climate

“…Ecological niche models of AHS and BTV have typically incorporated projections of temperature alongside multiple other environmental variables. One study has identified South America, the Indian subcontinent, and Australia as being the most suitable areas for AHS in future climates (Assefa et al 2022) and another suggested BTV will broaden in central Africa, the United States, and western Russia (Guis et al 2012).…”

Environmental and Historical Determinants of African Horse Sickness: Insights from Predictive Modeling

“…Beyond climatic factors, land cover and altitude are also instrumental in understanding the spread of AHS (17, 32, 33). These elements contribute to the creation of suitable habitats for vectors, thereby influencing the distribution and intensity of AHS outbreaks.…”

“…In light of these diverse environmental influences (17, 33, 41), our study aims to integrate these varied environmental factors into a cohesive model. By employing a global-scale approach and focusing on variable identification, we endeavor to elucidate the general laws governing the spread of AHS and address the geographical variability in prediction accuracy.…”