Bayesian inference across multiple models suggests a strong increase in lethality of COVID-19 in late 2020 in the UK.


Type
Article
Change log
Authors
Brorson, Erik 
Bankes, William 
Cates, Michael E 
Jack, Robert L 
Abstract

We apply Bayesian inference methods to a suite of distinct compartmental models of generalised SEIR type, in which diagnosis and quarantine are included via extra compartments. We investigate the evidence for a change in lethality of COVID-19 in late autumn 2020 in the UK, using age-structured, weekly national aggregate data for cases and mortalities. Models that allow a (step-like or graded) change in infection fatality rate (IFR) have consistently higher model evidence than those without. Moreover, they all infer a close to two-fold increase in IFR. This value lies well above most previously available estimates. However, the same models consistently infer that, most probably, the increase in IFR preceded the time window during which variant B.1.1.7 (alpha) became the dominant strain in the UK. Therefore, according to our models, the caseload and mortality data do not offer unequivocal evidence for higher lethality of a new variant. We compare these results for the UK with similar models for Germany and France, which also show increases in inferred IFR during the same period, despite the even later arrival of new variants in those countries. We argue that while the new variant(s) may be one contributing cause of a large increase in IFR in the UK in autumn 2020, other factors, such as seasonality, or pressure on health services, are likely to also have contributed.

Description
Keywords
Bayes Theorem, COVID-19, Humans, Models, Statistical, Prognosis, Quarantine, SARS-CoV-2, Severity of Illness Index, Survival Rate, United Kingdom
Journal Title
PLoS One
Conference Name
Journal ISSN
1932-6203
1932-6203
Volume Title
16
Publisher
Public Library of Science (PLoS)
Sponsorship
European Research Council (740269)