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ABSTRACT

Recent strides in the early warning and prediction of zoonotic diseases underscore a noteworthy shift towards harnessing advanced technologies and data-driven methodologies. The pivotal role of machine learning and artificial intelligence is evident in their contribution to the analysis of extensive datasets, enabling the identification of patterns and trends indicative of potential outbreaks. Notably, the integration of genomic data into predictive models has significantly enhanced the precision and speed of disease detection, facilitating early identification of emerging pathogens and their variants. The development of sophisticated models that incorporate environmental factors, human behaviour, and global travel patterns has further improved prediction accuracy, enabling more effective public health responses. Additionally, syndromic surveillance systems, focusing on monitoring non-specific pre-diagnosis medical data, have evolved to provide real-time insights into disease trends, contributing to early warnings. Embracing a One Health approach, which considers the interconnectedness of human, animal, and environmental health, has become integral to these advancements. In essence, these collective advancements represent a significant leap forward in our proactive ability to anticipate and mitigate the impact of zoonotic diseases on a global scale.

Keywords: Data-driven methodologies, early warning, Genomic surveillance, machine learning, Zoonoses