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February 2, 2026Open Access

Correction: 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

CBCam BowieSomerset County Council TPThomas ParrUniversity of Oxford

Key Points

Assess predictive validity of dynamic causal models in infectious disease epidemiology during the COVID-19 epidemic.
Applied dynamic causal modeling techniques to COVID-19 data in the UK
Analyzed incidence rates from 2020 to 2024
Evaluated the accuracy of predictions made by the models
Dynamic causal models showed significant predictive validity during the COVID-19 epidemic
Results highlight the importance of modeling in public health responses
Findings suggest potential improvements in forecasting disease spread through better modeling techniques

Abstract

Keywords: coronavirus, compartmental models, epidemiology-descriptive, incidence, public health methodology, dynamic causal modeling Citation: Bowie C and Friston K (2026) Correction: 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. Front. Public Health 13:1766577. doi: 10.3389/fpubh.2025.1766577 Received: 12 December 2025; Accepted: 29 December 2025; Published: 30 January 2026. Edited and reviewed by: Maximilian Pangratius de Courten , Victoria University, Australia Copyright © 2026 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) and the copyright owner(s) 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, cam.bowie1@gmail.com

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