Microbial Surveillance and Predictive Modeling for Zoonotic Disease Prevention in One Health Systems

The growing interactions between humans, animals and ecosystems, arising from environmental change, agriculture and expansion, contact with wildlife, and globalization, continue to make zoonotic diseases a major health concern worldwide. Effective prevention requires moving away from a reactive approach to outbreak response and towards a proactive approach that is based on prediction and early intervention. It emphasizes the need for multi-disciplinary surveillance – human, animal, and environmental health – data – that can detect emerging threats and track pathogen evolution. Improvements in microbial detection tools such as genomic sequencing, metagenomics, molecular diagnostics, and biosensor platforms have improved the speed at which pathogens are identified, the ability to trace their origins and the ability to identify and characterize antimicrobial resistance. The use of Geographic information systems and digital health technologies provides additional support for surveillance through spatial risk assessment, real-time monitoring and targeted public health measures. Experience from worldwide surveillance programs shows that predictive systems work best when they have consistent protocols, multi-sector collaboration and a permanent infrastructure. Barriers to providing full implementation of One Health surveillance networks still exist, such as limited resources, data system disruptions, governance issues, data privacy issues, and technology capacity gaps. A key to future advances will be the creation of interoperable platforms, models based on explainable artificial intelligence, better international coordination, and integration of genomic, ecological and epidemiological information. In conclusion, predictive microbial surveillance is a game-changer in the fight against zoonotic pathogens and in creating resilient health systems worldwide.