id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
cord-332922-2qjae0x7	Mbuvha, Rendani	Bayesian inference of COVID-19 spreading rates in South Africa	2020-08-05		.txt	text/plain	3224	169	52	In this work, we perform Bayesian parameter inference using Markov Chain Monte Carlo (MCMC) methods on the Susceptible-Infected-Recovered (SIR) and Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological models with time-varying spreading rates for South Africa. The results find two change points in the spreading rate of COVID-19 in South Africa as inferred from the confirmed cases. The second change point coincides with the start of a state-led mass screening and testing programme which has highlighted community-level disease spread that was not well represented in the initial largely traveller based and private laboratory dominated testing data. In this work we combine Bayesian inference with the compartmental SEIR and SIR models to infer time varying spreading rates that allow for quantification of the impact of government interventions in South Africa. SIR and SEIR model parameter inference was performed using confirmed cases data up to and including 20 April 2020 and MCMC samplers described in the methodology section.	./cache/cord-332922-2qjae0x7.txt	./txt/cord-332922-2qjae0x7.txt