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10.1371/journal.pone.0237126 http://scihub22266oqcxt.onion/10.1371/journal.pone.0237126 32756608!7406053!32756608     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+One 2020 ; 15 (8): e0237126 Nephropedia Template TP {{tp|p=32756608|t=2020. Bayesian inference of COVID-19 spreading rates in South Africa |pdf=|usr=}}{{32756608}} gab.com Text Bayesian inference of COVID-19 spreading rates in South Africa JO: PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32756608 #FOAvip The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. 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 first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. 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. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease. Twit Text FOAVip Bayesian inference of COVID-19 spreading rates in South Africa ????PLoS One http://www.kidney.de/pget.php?&tw=1&pmid=32756608 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32756608 #FOAvip English Wikipedia <ref>{{Cite pmid|32756608}}</ref> Bayesian inference of COVID-19 spreading rates in South Africa #MMPMID32756608 Mbuvha R; Marwala T PLoS One 2020[]; 15 (8): e0237126 PMID32756608show ga The Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has highlighted the need for performing accurate inference with limited data. Fundamental to the design of rapid state responses is the ability to perform epidemiological model parameter inference for localised trajectory predictions. 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 first change point coincides with state enactment of a travel ban and the resultant containment of imported infections. 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. The results further suggest that due to the likely effect of the national lockdown, community level transmissions are slower than the original imported case driven spread of the disease. |*Bayes Theorem[MESH] |Algorithms[MESH] |COVID-19[MESH] |Coronavirus Infections/diagnosis/*epidemiology/transmission/virology[MESH] |Humans[MESH] |Markov Chains[MESH] |Monte Carlo Method[MESH] |Pandemics[MESH] |Pneumonia, Viral/diagnosis/*epidemiology/transmission/virology[MESH]Bayesian inference of COVID-19 spreading rates in South Africa (epmc).pdf DeepDyve Pubget Overpricing