id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-034181-ji4empe6	Saqib, Mohd	Forecasting COVID-19 outbreak progression using hybrid polynomial-Bayesian ridge regression model	2020-10-23		.txt	text/plain	4637	277	55	The model is formulated using Bayesian Ridge Regression hybridized with an n-degree Polynomial and uses probabilistic distribution to estimate the value of the dependent variable instead of using traditional methods. Furthermore, one issue occurs when working with time-series data (as COVID-19 confirmed cases) is over-fitting particularly when estimating models with large numbers of parameters over relatively short periods and the solution to the over-fitting problem, is to take a Bayesian approach (using Ridge Regularization) which allows us to impose certain priors on depended variables [26] . In the Bayesian regression approach, we can take into account Other models are developed with good accuracy but as well as data become available, those entire algorithms will not able to survive without a few evaluations due to the dynamic nature of pandemic escalation of the COVID-19 but the proposed model corrects the distributions for model parameters and forecasting results using parameters distributions.	./cache/cord-034181-ji4empe6.txt	./txt/cord-034181-ji4empe6.txt