Lumpy skin disease virus: assessing zoonotic potential through machine learning

초록

Lumpy Skin Disease Virus (LSDV), the causative agent of LSD in cattle, has emerged as a significant threat to global livestock industries, prompting concerns about its potential for zoonotic transmission. While historically considered a non-zoonotic pathogen, recent outbreaks and limited evidence necessitate a proactive approach to risk assessment. This study employed a machine learning model to evaluate the zoonotic potential of LSDV by analyzing complete genomic sequences. Our analysis included a comparative assessment with closely related poxviruses, including Sheeppox virus, Swinepox virus, Goatpox virus, Cowpox virus, Monkeypox virus, and Orf virus, to provide a comprehensive understanding of LSDV’s zoonotic risk. Our findings indicate a minimal zoonotic risk associated with LSDV, aligning with the limited reports of human infections. The machine learning model, utilizing a curated dataset of viral genomic sequences, generated zoonotic prediction scores for LSDV that consistently fell within the low to medium range. This research underscores the importance of integrating machine learning in predicting and understanding potential viral mutations, advocating for proactive surveillance and predictive modeling in managing emerging zoonoses. By combining genomic surveillance with advanced computational tools, we can enhance our preparedness and response to potential zoonotic threats, ensuring public health security in the face of evolving infectious diseases. However, considering the virus’s evolving nature and global spread, continuous monitoring for genetic variations remains crucial. This research underscores the importance of integrating machine learning in predicting and understanding potential viral mutations, advocating for proactive surveillance and predictive modeling in managing emerging zoonoses.

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