id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-202376-440zapcw	Wilder, Bryan	Tracking disease outbreaks from sparse data with Bayesian inference	2020-09-12		.txt	text/plain	5808	347	55	The COVID-19 pandemic provides new motivation for a classic problem in epidemiology: estimating the empirical rate of transmission during an outbreak (formally, the time-varying reproduction number) from case counts. Our model places a Gaussian process prior over the unknown reproduction number at each time step and models observations sampled from the distribution of a specific testing program. We now depart from the standard disease model used in previous work and describe a wide-ranging set of examples for how our framework can accommodate models of the process which generates the observed data from the latent (unknown) true infections. However, is complicated by the fact that x is determined by a large number of discrete latent variables, primarily n (the time series of infections) and {t i convert , t i revert } N i=1 , the times when each individual tests positive.	./cache/cord-202376-440zapcw.txt	./txt/cord-202376-440zapcw.txt