Analysis and prediction of COVID-19 spreading through Bayesian modelling with a case study of Uttar Pradesh, India

Predicting the dynamics of COVID-19 cases is imperative to enhance the health care system’s capacity, monitor the effects of policy interventions, and control the transmission. With this view, this paper examines the transmission process of the COVID-19 employing three types of confirmed, deceased, and recovered cases in Uttar Pradesh, India. We demonstrated an approach that has the power to sufficiently predict the number of confirmed, deceased, and recovered cases of COVID-19 in the near future, given the past occurrences. We used the logistic and Gompertz non-linear regression model under the Bayesian setup. In this regard, we built the prior distribution of the model using information obtained from some other states of India, which have already reached the advanced stage of COVID-19. This analysis did not consider any changes in government control measures.