BAYESIAN INFERENCE TO PREDICT NEXT LOWER RECORDS: AN APPLICATION IN EPIDEMIOLOGICAL MODELING

Various fluctuations were observed in the reported deaths and confirmed cases of COVID-19. It is, however, essential to predict lower record rates of morbidity and mortality based on the available data. In this study, we use a Bayesian setting to obtain the point and interval predictions of the future lower records of confirmed cases and deaths. To do this, we adopt a Markov Chain Monte Carlo (MCMC) algorithm for computing Bayes predictors and explain all the tricks required to implement them in detail. We also provide real examples of applications of the proposed algorithm to predict future lower records. The results demonstrate the proximity of the predicted values to the true lower records of datasets.