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

Area of Science:

• Veterinary Epidemiology
• Disease Modeling
• Infectious Disease Dynamics

Background:

• Vesicular stomatitis (VS) is a significant vector-borne livestock disease caused by the vesicular stomatitis New Jersey virus (VSNJV).
• Accurate modeling of VSNJV transmission is crucial for effective control strategies.
• Previous models may not fully capture the complexities of VS outbreaks, including vector-host interactions and detection biases.

Purpose of the Study:

• To apply an SEIR-SEI compartmental model for the first time to analyze VSNJV transmission dynamics.
• To investigate the 2014-2015 VSNJV outbreak in the United States.
• To estimate key epidemiological parameters and assess the impact of underreporting and heterogeneous competence.

Main Methods:

• Development and application of an SEIR-SEI compartmental model integrating vertebrate and insect vector populations.
• Incorporation of heterogeneous competence within populations and observation bias.
• Utilizing Bayesian inference and Markov chain Monte Carlo (MCMC) methods for parameter estimation.

Main Results:

• The model estimated significant underreporting, with only 10-24% of VSNJV infections being documented.
• Approximately 23% of documented infections presented with clinical symptoms.
• Key epidemiological parameters like force of infection and effective reproduction number (Rt) were estimated.

Conclusions:

• Including host competence and imperfect detection in disease models is vital for accurately depicting outbreak dynamics.
• The findings highlight the need for enhanced surveillance and targeted interventions to manage VS.
• This SEIR-SEI model serves as a foundational tool for future, more complex VS dynamic studies.