Speaker
Description
We study the detection of a contagious disease using a stochastic SVIR (susceptible–vaccinated–infected–removed) epidemic model. In this framework, vaccination is introduced as a preventive measure. However, the administered vaccine may fail to protect some individuals, and vaccine-induced immunity may wane over time.
The epidemic dynamics of this model are described through a continuous-time Markov chain, which allows us to analyze the time required for disease detection, the total number of infections that occur before confirmation, and the number of infections arising within the vaccinated population due to vaccine failure. By exploiting the transition structure of the underlying stochastic process, we derive recursive schemes for computing the moments and probabilities of these quantities.
The theoretical results are illustrated in the setting of outbreaks of foot-and-mouth disease in cattle. We consider several scenarios to show how imperfect vaccination and detection delays influence epidemic dynamics during the early stages of an outbreak.
