Prev Vet Med. 2026 Jan 15;248:106785. doi: 10.1016/j.prevetmed.2026.106785. Online ahead of print.
ABSTRACT
Syndromic surveillance, which monitors clinical or production data as potential indicators of disease, can complement existing diagnostic testing strategies for a more comprehensive surveillance system. Consistently recorded mortality data with established identification and traceability routes across cattle sectors could be useful indicators to monitor in a syndromic surveillance system. Ireland is progressing toward the eradication of bovine viral diarrhoea (BVD) virus following a programme initiated in 2013 to identify and remove calves that test positive for BVD. As the country prepares for BVD-free status under the EU Animal Health Law, stakeholders must consider strategies to detect possible re-emergence. Historical data from the eradication programme provides a unique opportunity to evaluate mortality-based syndromic surveillance for this purpose. This study aimed to develop a syndromic surveillance model based on calf mortality data and evaluate its use for early detection of BVD re-emergence in Ireland. For years 2014 through 2023, mixed-effects Cox proportional hazards models were built using calf mortality up to 100 days of age. Herd-level frailty estimates were extracted from these models for each year, which were then clustered to identify subgroups of herds with distinct temporal patterns in herd-level mortality hazard. Four separate thresholds were used to flag herds with increased calf mortality hazard. Overall, these flags demonstrated high specificity (86-92 %) but low sensitivity (11-22 %) for herd-level BVD status, suggesting that this approach alone would not reliably detect BVD re-emergence. Nonetheless, this method could support Ireland’s ability to achieve and sustain BVD-free status while providing valuable insights for similar surveillance efforts more broadly. This methodology is adaptable to other species, diseases, and syndromes, making it a versatile tool for animal health surveillance.
PMID:41564497 | DOI:10.1016/j.prevetmed.2026.106785
