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

## TL;DR

This study uses a new SEIR-SEI model to analyze the 2014–2015 US Vesicular Stomatitis outbreak, revealing significant underreporting and the need for improved surveillance.

## Contribution

The first application of an SEIR-SEI model to study VSNJV transmission dynamics, incorporating host and vector competence and detection bias.

## Key findings

- Only 10–24% of infections were documented during the outbreak.
- 23% of documented infections presented with clinical symptoms.
- Including competence and imperfect detection is crucial for accurate outbreak modeling.

## Abstract

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.