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Foot-and-mouth disease (FMD) is a severe, highly contagious viral disease of livestock that has a significant economic impact on domestic animals and threatens wildlife survival in China and border countries. However, effective surveillance and prevention of this disease is often incomplete and unattainable due to the cost, the great diversity of wildlife hosts, the changing range and dynamics, and the diversity of FMDV. In this study, we used predictive models to reveal the spread and risk of FMD in anticipation of identifying key nodes to control its spread. For the first time, the spatial distribution of FMD serotype O was predicted in western China and border countries using a niche model, which is a combination of eco-geographic, human, topographic, and vegetation variables. The transboundary least-cost pathways (LCPs) model for ungulates in the study area were also calculated. Our study indicates that FMD serotype O survival is seasonal at low altitudes (March and June) and more sensitive to temperature differences at high altitudes. FMD serotype O risk was higher in Central Asian countries and both were highly correlated with the population variables. Ten LCPs were obtained representing Pakistan, Kazakhstan, Kyrgyzstan, and China. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 infections in animals have been reported globally. However, the understanding of the complete spectrum of animals susceptible to SARS-CoV-2 remains limited. The virus's dynamic nature and its potential to infect a wide range of animals are crucial considerations for a One Health approach that integrates both human and animal health. This study introduces a bioinformatic approach to predict potential susceptibility to SARS-CoV-2 in both domestic and wild animals. By examining genomic sequencing, we establish phylogenetic relationships between the virus and its potential hosts. We focus on the interaction between the SARS-CoV-2 genome sequence and specific regions of the host species' ACE2 receptor. We analyzed and compared ACE2 receptor sequences from 29 species known to be infected, selecting 10 least common amino acid sites (LCAS) from key binding domains based on similarity patterns. Our analysis included 49 species across primates, carnivores, rodents, and artiodactyls, revealing complete consistency in the LCAS and identifying them as potentially susceptible. We employed the LCAS similarity pattern to predict the likelihood of SARS-CoV-2 infection in unexamined species. This method serves as a valuable screening tool for assessing infection risks in domestic and wild animals, aiding in the prevention of disease outbreaks. [ABSTRACT FROM AUTHOR]