African horse sickness (AHS) is a transboundary and non-contagious
arboviral infectious disease of equids. Infected Culicoides biting
midges can spread the African horse sickness virus, and Culicoides
imicola (C.imicola) is one of the important transmission vectors. The
disease has spread without any warning from the sub-Saharan Africa
towards the Southeast Asian countries. Therefore, it is imperative to
predict the distribution of the AHS infection risk along the
Sino–Southeast Asian borders. The reported AHS outbreaks were extracted
from the archive of the Food and Agriculture Organization from December
22, 2005 to September 1, 2020. The occurrence records of C.imicola were
mainly obtained from published literature. Subsequently, the maximum
entropy algorithm was used to model AHS and C.imicola separately and to
research the relationship among bioclimate variables, land cover
characterization, horse distribution density, and the prevalence of AHS
infection. Finally, we combined the AHS risk prediction with the
suitability map of C.imicola to model the risk areas for AHS occurrence
in Mainland China. The models showed the mean area under the curve (AUC)
as 0.935 and 0.910 for AHS and C.imicola, respectively. Using jackknife
analysis, we determined the important factors affecting the AHS outbreak
as horse distribution density, mean temperature of the wettest quarter,
and precipitation of the coldest quarter. The mean temperature of
coldest quarter contributed most to the occurrence of C.imicola,
followed by precipitation of coldest quarter and global land cover
characterization. The overlay of the AHS and C.imicola prediction map
shows that the areas southwest of Hainan and southeast of Fujian are at
high risk of AHS occurrence under current conditions. Furthermore, the
border sectors of Yunnan and Guangxi also presented relatively high
risk.