Uncovering biosecurity gaps: risk factors for PRRSV seropositivity in Costa Rican pig farms identified through machine learning

Abstract

Background Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) continues to impose significant economic losses on pig production globally. In Costa Rica, where the virus is endemic, there is limited knowledge of the farm-level risk factors influencing PRRSV spread. This study aimed to identify biosecurity factors associated with PRRSV seroprevalence in Costa Rican pig farms. Methods A cross-sectional survey was conducted on 21 pig farms across Costa Rica. Data on farm management and biosecurity practices were collected using a structured questionnaire and linked to PRRSV seroprevalence data from a companion study. Logistic regression, and machine learning methods like LASSO (Least Absolute Shrinkage and Selection Operator), and Random Forest models were used to identify significant risk factors associated with herd-level PRRSV positivity. Results Three key risk factors were consistently identified by both LASSO and Random Forest models: historical controlled exposure to PRRSV, restrictions on employee access to the farm, and restrictions on employee visits to other pig farms. Additional risk factors identified included topography, disinfection practices for transport vehicles, sanitation measures for visitors, boot and clothing protocols, and feedback procedures. Farms with a history of controlled exposure had an odds ratio of 90 (95% CI: 7.6–3,550) for being PRRSV-positive. Conclusion The findings underscore the importance of internal and external biosecurity measures, particularly in relation to personnel movement and intentional exposure practices. Modeling approaches such as LASSO and Random Forest provided complementary insights into PRRSV risk factors in a tropical production setting. These insights can guide tailored interventions to reduce PRRSV transmission in Costa Rica and similar regions.