Journal of Statistics Applications & Probability


Acute encephalitis syndrome(AES) most commonly affects children and young adults and can lead to considerable morbidity and mortality. In June 2019, the outbreak of acute encephalitis syndrome occurred in Muzaffarpur district and their neighbouring district of Bihar. This paper presents the Bayesian survival analysis of AES data of the Muzaffarpur district. AES data extracted from the SKMCH and KM hospital of Muzaffarpur. The Weibull, Log-normal, and Exponential, these survival models have been used for fitting of AES data with the help of brms packages of R and compared these models with the Leave one out cross-validation. brms package uses the Hamiltonian Monte Carlo(HMC) sampler and its extension, no-U-turn sampler (NUTS) algorithm of MCMC, for the simulation study. In addition, the Logistic regression model is used to predict the risk of death on the basis of observed characteristics or covariates.

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