ArticleScientific reports2022
Dynamic causal modelling of COVID-19 and its mitigations.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- The Free Energy Principle and Free Markets.Entropy (Basel, Switzerland) · 2026Article
- Chaotic and complex dynamics expose the limits of counterfactual reasoning.Scientific reports · 2026Article
- A scalable variational method for estimating the latent infection-rate field of an outbreak.PloS one · 2026Article
- A spatiotemporal transmission simulator for respiratory infectious diseases and its application to COVID-19.Infectious Disease Modelling · 2025Article
- 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.Frontiers in public health · 2025Article
- Estimating the value of face coverings during the COVID-19 epidemic: a dynamic causal modelling study.BMJ public health · 2025Article
- Reliability of dynamic causal modelling of resting-state magnetoencephalography.Human brain mapping · 2024Article
- Country-report pattern corrections of new cases allow accurate 2-week predictions of COVID-19 evolution with the Gompertz model.Scientific reports · 2024Article
- A follow up report validating long term predictions of the COVID-19 epidemic in the UK using a dynamic causal model.Frontiers in public health · 2024Article
- Linking fast and slow: The case for generative models.Network neuroscience (Cambridge, Mass.) · 2024Review
- Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity.Network neuroscience (Cambridge, Mass.) · 2024Article
- Understanding the leading indicators of hospital admissions from COVID-19 across successive waves in the UK.Epidemiology and infection · 2023Article
- Article
- Multiscale parcellation of dynamic causal models of the brain.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
3 authors.
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Abstract
This technical report describes the dynamic causal modelling of mitigated epidemiological outcomes during the COVID-9 coronavirus outbreak in 2020. Dynamic causal modelling is a form of complex system modelling, which uses 'real world' timeseries to estimate the parameters of an underlying state space model using variational Bayesian procedures. Its key contribution-in an epidemiological setting-is to embed conventional models within a larger model of sociobehavioural responses-in a way that allows for (relatively assumption-free) forecasting. One advantage of using variational Bayes is that one can progressively optimise the model via Bayesian model selection: generally, the most likely models become more expressive as more data becomes available. This report summarises the model (on 6-Nov-20), eight months after the inception of dynamic causal modelling for COVID-19. This model-and its subsequent updates-is used to provide nowcasts and forecasts of latent behavioural and epidemiological variables as an open science resource. The current report describes the underlying model structure and the rationale for the variational procedures that underwrite Bayesian model selection.
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