id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-184685-ho72q46e	Huang, Tongtong	Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates	2020-08-10		.txt	text/plain	4849	244	46	We present the development of a forecasting model using local fine-grained hospital-level data to track the changes in hospitalization and mortality rates owing to reopening orders in the greater Houston area encompassing nine counties in the state of Texas, USA. We demonstrated our new approach using a policy-aware risk-Stratified Susceptible-Infectious-Recovered Hospitalization-Critical-Dead (SSIR-HCD) model, which compared favorably to existing methods (including our neural network latent space modeling, a nonlinear extension of SIR-HCD). • Epidemiology based dynamic models based on grouping populations into a discrete set of compartments (i.e., states), and defining ordinary differential equations (ODE) rate equations describing the movement of people between compartments: SEIR (Susceptible, Exposed, Infected, Recovered) models and their myriad variants are examples in this category. Our SSIR-HCD model forecasts fine-grained COVID-19 hospitalization and mortality by accounting for the impact of local policies.	./cache/cord-184685-ho72q46e.txt	./txt/cord-184685-ho72q46e.txt