A retrospective assessment of forecasting the peak of the SARS-CoV-2 Omicron BA.1 wave in England
Year: 2024
Type: article
Abstract: We discuss the invasion of the Omicron BA.1 variant into England as a paradigm for real-time model fitting and projection. Here we use a mixture of simple SIR-type models, analysis of the early data and a more complex age-structure model fit to the outbreak to understand the dynamics. In particular, we highlight that early data shows that the invading Omicron variant had a substantial growth advantage over the resident Delta variant. However, early data does not allow us to reliably infer other key epidemiological parameters-such as generation time and severity-which influence the expected peak hospital numbers. With more complete epidemic data from January 2022 are we able to capture the true scale of the epidemic in terms of both infections and hospital admissions, driven by different infection characteristics of Omicron compared to Delta and a substantial shift in estimated precautionary behaviour during December. This work highlights the challenges of real time forecasting, in a rapidly changing environment with limited information on the variant's epidemiological characteristics.
Source: PLoS Computational Biology
Authors: Matt J. Keeling, Louise Dyson
Institution: University of Warwick
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,926
Awards: G1088240470, G1650259125, G2331623591, G4936643394, Mathematical modeling and adaptive control to inform real time decision making for the COVID-19 pandemic at the local, regional and national scale, COVID-19 Modelling Consortium: quantitative epidemiological predictions in response to an evolving pandemic, G8415457339
OpenAlex ID: W4402742343