Key result
A stochastic SIR compartmental model evaluated the time required for disease detection and incidence rates before detection for foot-and-mouth disease in cattle.
This study presents a stochastic SIR compartmental model to analyze the time to detection and incidence rates of vaccine-preventable diseases, with an application to foot-and-mouth disease in cattle.
May guide booster timing in stochastic SIR models; leaves open empirical validation for surveillance.
In this study, we focus on the detection of a vaccine‐preventable communicable disease using a stochastic SIR compartmental model, which includes an additional compartment for individuals protected by vaccination. Although the vaccine is administered as a preventive measure before the onset of the disease, its efficacy diminishes with time, which requires booster doses for susceptible individuals. Using a continuous‐time Markov chain, we examine the time required for disease detection (i.e., full confirmation of the disease) and analyze the incidence rates before detection, both within the vaccinated group and throughout the population. To illustrate the usefulness of our theoretical derivations and algorithmic schemes, we will present a numerical study involving the above descriptors for outbreaks of foot‐and‐mouth disease (FMD) in cattle.
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Lopez‐Herrero et al. (2025) studied Foot-and-mouth disease (FMD) in cattle. Vaccination was evaluated on Time required for disease detection and incidence rates before detection. A stochastic SIR compartmental model evaluated the time required for disease detection and incidence rates before detection for foot-and-mouth disease in cattle.
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