id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-208252-e0vlaoii	Calvetti, Daniela	Bayesian dynamical estimation of the parameters of an SE(A)IR COVID-19 spread model	2020-05-09		.txt	text/plain	7814	351	49	A Bayesian particle filtering algorithm is used to update dynamically the relevant cohort and simultaneously estimate the transmission rate as the new data on the number of new infections and disease related death become available. When we apply the model and particle filter algorithm to COVID-19 infection data from several counties in Northeastern Ohio and Southeastern Michigan we found the proposed reproduction number $R_0$ to have a consistent dynamic behavior within both states, thus proving to be a reliable summary of the success of the mitigation measures. The equilibrium value, which can be analytically calculated from the model parameters, corresponds well to the model-based estimated ratio and can be used to define a dynamically changing effective basic reproduction number R 0 for the epidemic, facilitating the comparison of model predictions with other models.	./cache/cord-208252-e0vlaoii.txt	./txt/cord-208252-e0vlaoii.txt