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. 2025 May 2:13:1573783.
doi: 10.3389/fpubh.2025.1573783. eCollection 2025.

Dynamic causal models in infectious disease epidemiology-an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024

Cam Bowie  1 , Karl Friston  2
Affiliations

Dynamic causal models in infectious disease epidemiology-an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024

Cam Bowie et al. Front Public Health. .

Abstract

This technical report addresses the predictive validity of long-term epidemiological forecasting based upon dynamic causal modeling. It uses complementary prospective and retrospective analyses. The prospective analysis completes a series of (annual) reports comparing predictions with subsequent outcomes (i.e., cases, deaths, hospital admissions and Long COVID) reported a year later. Predictive validity is then addressed retrospectively by examining predictions at various points during the pandemic, in relation to actual outcomes at three, six and 12 months after the predictions were evaluated. This analysis suggests that-with a sufficiently expressive dynamic causal model-three, six and 12 month projections can be remarkably accurate (to within 10% or less of observed outcomes) at certain phases of the epidemic: namely, the initial phase-before the emergence of highly transmissible variants-and toward the end of the pandemic, when slow fluctuations in transmissibility and virulence can be estimated more precisely. However, the predictive accuracy in the intervening periods are compromised, to the extent that some forecasts only remain within their Bayesian credible intervals for 3 months. We provide a quantitative analysis of predictive accuracy for future reference and discuss the implications for epidemiological modeling, and forecasting, of this sort.

Keywords: compartmental models; coronavirus; dynamic causal modeling; epidemiology-descriptive; incidence; public health methodology.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

Figure 1
Figure 1
A comparison of the trend in incidence of new COVID-19 cases in the winter months of 2023 and 2024 between a UKSHA (A) (left panel) estimate and our projection (B) (right panel).
Figure 2
Figure 2
Model run using data from February 2020 to 30th September 2020.
Figure 3
Figure 3
Model run using data from February 2020 to 30th September 2021.
Figure 4
Figure 4
Model run using data from February 2020 to 30th September 2022.
Figure 5
Figure 5
Model run using data from February 2020 to 30th September 2023.
See this image and copyright information in PMC

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References

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