African swine fever (ASF) is a tick-borne infectious disease initially described in Shenyang province China in 2018 but is now currently present nationwide. ASF has high infectivity and mortality rates, which often results in transportation and trade bans, and high expenses to prevent and control the, hence causing huge economic losses and a huge negative impact on the Chinese pig farming industry. Ecological niche modeling has long been adopted in the epidemiology of infectious diseases, in particular vector-borne diseases. This study aimed to establish an ecological niche model combined with data from ASF incidence rates in China from August 2018 to December 2021 in order to predict areas for African swine fever virus (ASFV) distribution in China. The model was developed in R software using the biomod2 package and ensemble modeling techniques. Environmental and topographic variables included were mean diurnal range (°C), isothermality, mean temperature of wettest quarter (°C), precipitation seasonality (cv), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), normalized difference vegetation index, wind speed (m/s), solar radiation (kJ /day), and elevation/altitude (m). Contribution rates of the variables normalized difference vegetation index, mean temperature of wettest quarter, mean precipitation of coldest quarter, and mean precipitation of warmest quarter were, respectively, 47.61%, 28.85%, 10.85%, and 7.27% (according to CA), which accounted for over 80% of contribution rates related to variables. According to model prediction, most of areas revealed as suitable for ASF distribution are located in the southeast coast or central region of China, wherein environmental conditions are suitable for soft ticks’ survival. In contrast, areas unsuitable for ASFV distribution in China are associated with arid climate and poor vegetation, which are less conducive to soft ticks’ survival, hence to ASFV transmission. In addition, prediction spatial suitability for future ASFV distribution suggests narrower areas for ASFV spread. Thus, the ensemble model designed herein could be used to conceive more efficient prevention and control measure against ASF according to different geographical locations in China.

Lumpy skin disease (LSD) is a highly contagious disease in bovine animals. An outbreak of LSD can cause devastating economic losses to the cattle industry. To investigate the distribution characteristics of historical LSD epidemics, LSD was divided into four phases for directional distribution analysis based on trends in epidemic prevalence. Ecological niche models were developed for LSD as well as for two vectors (Stomoxys calcitrans and Aedes aegypti), and global predictive maps were generated for the probability of LSD occurrence and the potential distribution of the two LSD vectors. The models had good predictive performance (the AUC values were 0.894 for the LSD model, 0.911 for the S. calcitrans model, and 0.950 for the A. aegypti model). The LSD combined vector prediction map was generated by combining the distribution maps of Stomoxys calcitrans and Aedes aegyptiwith fuzzy overlay tool in ArcGIS. The LSD combined vector prediction map was combined with the LSD prediction map to generate the LSD vector transmission risk map. The eastern and northwestern regions of North America, the eastern and northern regions of South America, the central and southern regions of Africa, the southern region of Europe, the northwestern and southeastern regions of Asia, and the eastern region of Australia were predicted to provide suitable environmental conditions for the occurrence of LSD. Cattle density, buffalo density, and bio2 (mean diurnal range) were identified as key variables for the occurrence of LSD. The findings of this study can be useful to policymakers in developing and implementing preventive measures of LSD for the health of cattle and the cattle industry.