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SUMMARY:Bayesian estimation of the instant growth rate of SARS-CoV-2 posit
 ive cases in England and forecasting\, using Gaussian processes. - Dr Laur
 a Guzman Rincon\, University of Warick
DTSTART:20210908T150000Z
DTEND:20210908T160000Z
UID:TALK161902@talks.cam.ac.uk
CONTACT:Dr Ciara Dangerfield
DESCRIPTION:The growth rate estimation of SARS-CoV-2 positive cases is cru
 cial for understanding the evolution of the pandemic. We propose a method 
 for estimating the growth rate of the proportion of positive cases in Engl
 and and its local authorities. The proposed Bayesian model incorporates a 
 Gaussian process as a latent effect\, employed to compute the growth rate 
 and higher derivatives. This method does not make assumptions about genera
 tion times and can be adapted to different spatial geographies and populat
 ion subgroups. Also\, various forecasting methods are incorporated into th
 e model and tested using proper scoring rules.
LOCATION:Zoom
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