Modeling the 2014-2015 Vesicular Stomatitis Outbreak in the United States Using an SEIR-SEI Approach

• Published in: Viruses 2024-08, Vol.16 (8), p.1315
• Main Authors: Humphreys, John M, Pelzel-McCluskey, Angela M, Shults, Phillip T, Velazquez-Salinas, Lauro, Bertram, Miranda R, McGregor, Bethany L, Cohnstaedt, Lee W, Swanson, Dustin A, Scroggs, Stacey L P, Fautt, Chad, Mooney, Amber, Peters, Debra P C, Rodriguez, Luis L
• Format: Article
• Language: English
• Subjects: Animals; Arthropods; Bayes Theorem; Bayesian analysis; Cattle; compartmental model; Diagnosis; Disease Outbreaks; Disease prevention; Disease transmission; Diseases; Epidemics; Epidemiology; Foot & mouth disease; Health aspects; Insect Vectors - virology; Livestock; Livestock - virology; Markov chains; Mathematical models; Ordinary differential equations; Parameter estimation; Pathogens; Pest outbreaks; Physiological aspects; Physiology; Quarantine; Stomatitis; Trade restrictions; transmission; United States; United States - epidemiology; vector-borne diseases; vesicular stomatitis; Vesicular Stomatitis - epidemiology; Vesicular Stomatitis - virology; Vesicular stomatitis New Jersey virus - genetics
• ISSN: 1999-4915; 1999-4915
• DOI: 10.3390/v16081315

Vesicular stomatitis (VS) is a vector-borne livestock disease caused by the vesicular stomatitis New Jersey virus (VSNJV). This study presents the first application of an SEIR-SEI compartmental model to analyze VSNJV transmission dynamics. Focusing on the 2014-2015 outbreak in the United States, the model integrates vertebrate hosts and insect vector demographics while accounting for heterogeneous competency within the populations and observation bias in documented disease cases. Key epidemiological parameters were estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) methods, including the force of infection, effective reproduction number (Rt), and incubation periods. The model revealed significant underreporting, with only 10-24% of infections documented, 23% of which presented with clinical symptoms. These findings underscore the importance of including competence and imperfect detection in disease models to depict outbreak dynamics and inform effective control strategies accurately. As a baseline model, this SEIR-SEI implementation is intended to serve as a foundation for future refinements and expansions to improve our understanding of VS dynamics. Enhanced surveillance and targeted interventions are recommended to manage future VS outbreaks.