RECURRENT EPIDEMIC MODELING USING MCMC METHODS

摘要

We present the SIS epidemic model as a Binary Markov Process and employed Markov chain Monte Carlo (MCMC) techniques to obtain model parameters; the rate of infection and the clearance rate for recurrent infectious diseases. The model explicitly consists of an overall carriage transmission within the community at individual level. The joint posterior of the model parameters and the augmented data is explored by using Reversible Jump Markov chain Monte Carlo (RJMCMC) method. Further, we have discusseda few methods to understand the underlying mechanism that influences the spread of disease and predicting disease transmission through threshold parameters such as the Basic and Effective Reproduction Number. To locate the Optimum Vaccination Coverage,different levels of vaccination are considered. We have demonstrated this model for Pertussis (a whooping cough) disease by generating a small data set. From simulated data we found, the Critical Vaccination Coverage (Herd Immunity) as 88% with credibleinterval (CrI) ranges from 84% to 90%, whereas the study on Optimum Vaccination Coverage support 78.8% coverage is ample for disease control. Clearly, these results evince the efficiency of the model.