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ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Health Policy
Volume 12 - 2024 |
doi: 10.3389/fpubh.2024.1398297
This article is part of the Research Topic Global Infectious Disease Surveillance Technologies and Data Sharing Protocols View all 5 articles
A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a Dynamic Causal Model Authors
Provisionally accepted- 1 Retired, Axminster Devon, United Kingdom
- 2 University College London, London, England, United Kingdom
Background -this paper asks whether Dynamic Causal modelling (DCM) can predict the long-term clinical impact of the COVID-19 epidemic. DCMs are designed to continually assimilate data and modify model parameters, such as transmissibility of the virus, changes in social distancing and vaccine coverage-to accommodate changes in population dynamics and virus behaviour. But as a novel way to model epidemics do they produce valid predictions? We presented DCM predictions 12 months ago, which suggested an increase in viral transmission was accompanied by a reduction in pathogenicity. These changes provided plausible reasons why the model underestimated deaths, hospital admissions and acute-post COVID-19 syndrome by 20%. A further 12-month validation exercise could help to assess how useful such predictions are. Methods -we compared DCM predictions-made in October 2022-with actual outcomes over the twelvemonths to October 2023. The model was then used to identify changes in COVID-19 transmissibility and the sociobehavioural responses that may explain discrepancies between predictions and outcomes over this period. The model was then used to predict future trends in infections, long-Covid, hospital admissions and deaths over 12-months to October 2024, as a prelude to future tests of predictive validity. Findings -Unlike the previous predictions-which were an underestimate-the predictions made in October 2022 overestimated incidence, death and admission rates. This overestimation appears to have been caused by reduced infectivity of new variants, less movement of people and a higher persistence of immunity following natural infection and vaccination. Interpretation -despite an expressive (generative) model, with time-dependent epidemiological and sociobehavioural parameters, the model overestimated morbidity and mortality. Effectively, the model failed to accommodate the "law of declining virulence" over a timescale of years. This speaks to a fundamental issue in long-term forecasting: how to model decreases in virulence over a timescale of years? A potential answer may be available in a year when the predictions for 2024-under a model with slowly accumulating T-cell like immunity-can be assessed against actual outcomes.
Keywords: Dynamic causal model, Covid-19 mitigation measures, Acute-post COVID-19, hospital admissions, Mortality incidence
Received: 09 Mar 2024; Accepted: 23 Aug 2024.
Copyright: © 2024 Bowie and Friston. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Cam Bowie, Retired, Axminster Devon, United Kingdom
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