Serveur d'exploration sur la COVID en France

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Dynamic causal modelling of COVID-19.

Identifieur interne : 001104 ( Main/Exploration ); précédent : 001103; suivant : 001105

Dynamic causal modelling of COVID-19.

Auteurs : Karl J. Friston [Royaume-Uni] ; Thomas Parr [Royaume-Uni] ; Peter Zeidman [Royaume-Uni] ; Adeel Razi [Royaume-Uni, Australie] ; Guillaume Flandin [Royaume-Uni] ; Jean Daunizeau [France] ; Ollie J. Hulme [Danemark, Royaume-Uni] ; Alexander J. Billig [Royaume-Uni] ; Vladimir Litvak [Royaume-Uni] ; Rosalyn J. Moran [Royaume-Uni] ; Cathy J. Price [Royaume-Uni] ; Christian Lambert [Royaume-Uni]

Source :

RBID : pubmed:32832701

Abstract

This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.

DOI: 10.12688/wellcomeopenres.15881.2
PubMed: 32832701
PubMed Central: PMC7431977


Affiliations:


Links toward previous steps (curation, corpus...)


Le document en format XML

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<div type="abstract" xml:lang="en">This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model
<i>per se</i>
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<AbstractText>This technical report describes a dynamic causal model of the spread of coronavirus through a population. The model is based upon ensemble or population dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model
<i>per se</i>
can be optimised given timeseries data. Although the purpose of this paper is to describe a modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.</AbstractText>
<CopyrightInformation>Copyright: © 2020 Friston KJ et al.</CopyrightInformation>
</Abstract>
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<ForeName>Karl J</ForeName>
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<Affiliation>Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG, UK.</Affiliation>
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<Initials>T</Initials>
<Identifier Source="ORCID">https://orcid.org/0000-0001-5108-5743</Identifier>
<AffiliationInfo>
<Affiliation>Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG, UK.</Affiliation>
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<ForeName>Peter</ForeName>
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<Affiliation>Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG, UK.</Affiliation>
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<LastName>Razi</LastName>
<ForeName>Adeel</ForeName>
<Initials>A</Initials>
<Identifier Source="ORCID">https://orcid.org/0000-0002-0779-9439</Identifier>
<AffiliationInfo>
<Affiliation>Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG, UK.</Affiliation>
</AffiliationInfo>
<AffiliationInfo>
<Affiliation>Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University, Clayton, VIC, 3800, Australia.</Affiliation>
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<Initials>G</Initials>
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<LastName>Hulme</LastName>
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<Initials>OJ</Initials>
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<Affiliation>Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark.</Affiliation>
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<Affiliation>London Mathematical Laboratory, Hammersmith, London, UK.</Affiliation>
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<ForeName>Alexander J</ForeName>
<Initials>AJ</Initials>
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<Affiliation>Ear Institute, University College London, London, UK.</Affiliation>
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<Affiliation>Centre for Neuroimaging Science, Department of Neuroimaging, IoPPN, King's College London, London, UK.</Affiliation>
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<LastName>Price</LastName>
<ForeName>Cathy J</ForeName>
<Initials>CJ</Initials>
<AffiliationInfo>
<Affiliation>Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG, UK.</Affiliation>
</AffiliationInfo>
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<Initials>C</Initials>
<Identifier Source="ORCID">https://orcid.org/0000-0001-8281-4611</Identifier>
<AffiliationInfo>
<Affiliation>Wellcome Centre for Human Neuroimaging, University College London, London, WC1N 3BG, UK.</Affiliation>
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