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

ABSTRACT The pandemic of coronavirus disease 2019 (COVID-19) started in Wuhan, China, and spread worldwide. In India, COVID-19 cases increased rapidly throughout India. Various measures like awareness program, social distancing, and contact tracing have been implemented to control the COVID-19 outbreak. In the absence of any vaccine, the prediction of the confirmed, deceased, and recovered cases is required to enhance the health care system’s capacity and control the transmission. In this study, the cumulative and daily confirmed, deceased, and recovered cases in Uttar Pradesh, India, were analyzed. We used the logistic and Gompertz non-linear regression model using a Bayesian paradigm. We build the prior distribution of the model using information obtained from some other states of India, which are already reached at the advanced stage of COVID-19. Results from the analysis indicated that the predicted maximum number of confirmed, deceased, and recovered cases will be around 1157335, 5843, and 1145829 respectively. The daily number of confirmed, deceased, and recovered cases will be maximum at 104th day, 73rd day, and 124th day from 16 June 2020. Further from this analysis we can conclude that the COVID-19 will be over probably by early-June, 2021. The analysis did not consider any changes in government control measures. We hope this study can provide some relevant information to the government and health officials.