BACKGROUND Phlebotomine sandfly (Diptera: Psychodidae) is best known for its role as the vector of the leishmaniasis. Leishmaniasis is a global disease, currently known to be affecting 88 countries and 12 million people worldwide. RESULTS This study explored the impacts of climate factors and landscape on the local abundance of Phlebotomine sandfly in mainland China. A presence‐only modelling method was used to evaluate this species' habitat preferences from environmental factors. Jackknife analysis revealed that several biologically meteorological variables, including the maximum temperature of the warmest quarter, the precipitation in the driest month, the daily average temperature and daily precipitation, would significantly affect the existence of this species. Moreover, the existence of Phlebotomine sandfly was significantly associated with grassland and forest. CONCLUSION The results will improve scientific understanding of the risks of the spread of leishmaniasis over current infected areas and can be used to design more detailed surveillance programmes and more evidence‐based control planning for Phlebotomine sandfly and leishmaniasis. © 2019 Society of Chemical Industry

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.

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