id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-350001-pd2bnqbp	Liu, L.	Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility	2020-08-07		.txt	text/plain	5516	276	53	We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. On the other hand, compartmental models [e.g., 1, 6, 7] assume a flexible, causal story for the spread of a disease and can also incorporate mobility data as a covariate for predicting the time-varying infection rate of a disease. However, most often though we don't know the parameters of the model beforehand, but we do have some data that can provide a learning signal to fit the parameters, One such signal is the daily number of new cases of a disease, which can be predicted by a compartmental model as the change in I + R between each day.	./cache/cord-350001-pd2bnqbp.txt	./txt/cord-350001-pd2bnqbp.txt