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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.  

Preprint 

Speaker 

is a Research Fellow at the Department of Mathematics, University of Warwick, and a member of the . Her current research focuses on developing mathematical models to answer epidemiological questions related to the COVID-19 pandemic in the UK. As part of her work with JUNIPER, she has been routinely engaged in estimating the growth rate of COVID-19 cases at a small-scale level, studying the spatial dynamics of new variants and estimating their growth advantage. 

Laura has a PhD in Mathematics of Systems from the University of Warwick. As a doctoral candidate, she developed a statistical framework to detect outbreaks of Campylobacter infections using epidemiological and genetic data. Her research interests cover Gaussian processes, Bayesian hierarchical models, spatial statistics and information theory applied to real-world problems.

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