Epidemiological Patterns in Zoonotic Diseases

Abstract

Zoonotic diseases -- infections that transmit between animals and humans -- account for approximately 60% of all human infectious diseases and 75% of emerging infectious disease events, including the three most consequential pandemics of the 21st century: SARS (2003), influenza H1N1 (2009), and COVID-19 (2019). Despite their outsized burden, zoonotic disease surveillance remains fragmented between human and veterinary health systems, with minimal operational integration at the critical animal-human interface where spillover events originate. We analysed epidemiological patterns of 14 zoonotic diseases across 28 European countries from 2010 to 2022 using data from ECDC, EFSA, OIE/WOAH, and national veterinary and public health surveillance systems, encompassing 2.4 million human cases and 18.6 million animal surveillance records. We compared five surveillance and analytical frameworks: traditional separate human-animal reporting, integrated One Health surveillance, environmental sentinel monitoring, genomic phylogeographic tracking, and ML-driven spillover risk prediction. Our Zoonotic Surveillance Effectiveness Score (ZSES) integrates spillover detection timeliness, source attribution accuracy, outbreak prediction capability, cross-sectoral data integration, and prevention impact. ML-driven spillover prediction achieved the highest ZSES (0.926) by identifying high-risk geographic-temporal windows for spillover events with AUROC 0.84, while genomic phylogeographic tracking (ZSES 0.912) provided the most accurate source attribution, linking 78% of human outbreaks to specific animal reservoirs within 14 days.