id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-288884-itviia7v	Chandra, Vedant	Stochastic Compartmental Modelling of SARS-CoV-2 with Approximate Bayesian Computation	2020-04-01		.txt	text/plain	1555	120	61	We fit this model to the latest epidemic data with an approximate Bayesian computation (ABC) technique. Within this SIR-ABC framework, we extrapolate long-term infection curves for several regions and evaluate their steepness. Armed with the ability to generate stochastic infection and recovery curves from starting parameters, we turn to fitting the starting parameters from real-world epidemic data. We therefore employ an approximate Bayesian computation (ABC) technique to compare our simulations to observations and recover the posterior distributions of β and γ (Figure 1 ). The general goal of ABC is to sample the posterior distributions of simulation parameters such that the simulations match the observed data. Given a simulated epidemic and the observed data, we quantify the difference between both the infectious and recovered population curves to obtain a distance In this proof-of-concept study, we apply approximate Bayesian computation to fit stochastic epidemic models to real world data.	./cache/cord-288884-itviia7v.txt	./txt/cord-288884-itviia7v.txt