id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-304429-qmcrvufu	Deepmala,	Analysis and prediction of Covid-19 spreading through Bayesian modelling with a case study of Uttar Pradesh, India	2020-08-31		.txt	text/plain	3287	178	59	This study focuses on the analysis and the prediction of the epidemic situation of COVID-19 in the state of Uttar Pradesh, India, using logistic and Gompertz nonlinear regression model, which are accord with the statistical law of epidemiology. By using the results of the non-linear models fitted by least square estimation (LSE), we define the prior distribution of the parameters of the Bayesian non-linear models for estimating and predicting the cumulative and the daily confirmed, deceased, and recovered cases of Uttar Pradesh state. Figures 2, 3 and 4 show the cumulative and the daily number of confirmed cases, deceased cases, and recovered cases of COVID-19 in Uttar Pradesh respectively and the fitted curve by the Baysian non-linear regression model using the prior information. Also, Watanabe Akaike information criterion (WAIC) is computed from the fitting of Bayesian Gompertz and logistic models to the data of the cumulative confirmed cases, cumulative deceased cases, and cumulative recovered cases of COVID-19 in UP, India.	./cache/cord-304429-qmcrvufu.txt	./txt/cord-304429-qmcrvufu.txt