id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-347199-slq70aou	Safta, Cosmin	Characterization of partially observed epidemics through Bayesian inference: application to COVID-19	2020-10-07		.txt	text/plain	8406	455	54	The method is cast as one of Bayesian inference of the latent infection rate (number of people infected per day), conditioned on a time-series of Developing a forecasting method that is applicable in the early epoch of a partially-observed outbreak poses some peculiar difficulties. This infection rate curve is convolved with the Probability Density Function (PDF) of the incubation period of the disease to produce an expression for the time-series of newly symptomatic cases, an observable that is widely reported as "daily new cases" by various data sources [2, 5, 6] . 2, with postulated forms for the infection rate curve and the derivation of the prediction for daily new cases; we also discuss a filtering approach that is applied to the data before using it to infer model parameters.	./cache/cord-347199-slq70aou.txt	./txt/cord-347199-slq70aou.txt