A follow up report validating long term predictions of the Covid-19 epidemic in the UK using a dynamic causal model
Bibliographic Data
| ID | 22069386 |
|---|---|
| Authors | Cameron Bowie (0000-0002-6956-3691, Somerset County Council, corresponding author), Cam Bowie, Karl J Friston (0000-0001-7984-8909, National Hospital for Neurology and Neurosurgery), Karl Friston |
| Year | 2024 |
| Volume | 12 |
| Pages | 1398297-1398297 |
| Publication date | 2024-09-09 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1398297 |
| PMID | 39314791 |
| OpenAlex | W4402381395 |
| Language | EN |
| Citations received | 1 |
| References cited | 9 |
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 behavior. 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 12-months to October 2023. The model was then used to identify changes in COVID-19 transmissibility and the sociobehavioral 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 sociobehavioral 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
2019-20 coronavirus outbreak · Disease · Econometrics · Environmental health · Epidemic model · Outbreak · Pandemic · Population · Advanced Causal Inference Techniques · Computer Science · COVID-19 epidemiological studies · Mathematics · Medicine · vaccines and immunoinformatics approaches · Virology
A 12-month projection to September 2022 of the Covid-19 epidemic in the UK using a dynamic causal model
Using a Dynamic Causal Model to validate previous predictions and offer a 12-month forecast of the long-term effects of the Covid-19 epidemic in the UK
‘Dark matter’, second waves and epidemiological modelling
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |