id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-249569-78zstcag	KIm, T.	Prediction Regions for Poisson and Over-Dispersed Poisson Regression Models with Applications to Forecasting Number of Deaths during the COVID-19 Pandemic	2020-07-04		.txt	text/plain	10899	603	61	Motivated by the current Coronavirus Disease (COVID-19) pandemic, which is due to the SARS-CoV-2 virus, and the important problem of forecasting daily deaths and cumulative deaths, this paper examines the construction of prediction regions or intervals under the Poisson regression model and for an over-dispersed Poisson regression model. The real-life and practical application for which our methods will be applied is the construction of prediction regions for the daily and cumulative number of deaths due to COVID-19 in the US for a future date given only the daily deaths data until a current date. Summing up our observations from these simulation studies for this no-covariate or intercept only Poisson model, in terms of adapting to the estimation of the unknown rate λ,Γ 1 andΓ 5 possess the best performance among these six prediction regions in terms of achieving the nominal level, but they also tend to be longer than the others.	./cache/cord-249569-78zstcag.txt	./txt/cord-249569-78zstcag.txt