Stochastic SIR Household Epidemic Model with Misclassification

In this work, we developed a theoretical framework leading to misclassification of the final size epidemic data for stochastic SIR (Susceptible-Infective-Removed), household model, with false negative and positive misclassification probabilities. Maximum likelihood based algorithm is then employed its inference. We then analyzed compared estimates the two dimensional model those three four models associated misclassified over arrange theoretical parameters, local global infection rates corresponding proportion infected in permissible region, away from boundaries and misclassification The adequacies models to are examined. The four and three-dimensional found outperform two model on data.