University of Cambridge > Talks.cam > Worms and Bugs > Bayesian estimation of the instant growth rate of SARS-CoV-2 positive cases in England and forecasting, using Gaussian processes.

Bayesian estimation of the instant growth rate of SARS-CoV-2 positive cases in England and forecasting, using Gaussian processes.

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The growth rate estimation of SARS -CoV-2 positive cases is crucial for understanding the evolution of the pandemic. We propose a method for estimating the growth rate of the proportion of positive cases in England 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 generation times and can be adapted to different spatial geographies and population subgroups. Also, various forecasting methods are incorporated into the model and tested using proper scoring rules.

This talk is part of the Worms and Bugs series.

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