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Dynamic causal modelling of COVID-19

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 predict...

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Autores principales: Friston, Karl J., Parr, Thomas, Zeidman, Peter, Razi, Adeel, Flandin, Guillaume, Daunizeau, Jean, Hulme, Ollie J., Billig, Alexander J., Litvak, Vladimir, Moran, Rosalyn J., Price, Cathy J., Lambert, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000 Research Limited 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431977/
https://www.ncbi.nlm.nih.gov/pubmed/32832701
http://dx.doi.org/10.12688/wellcomeopenres.15881.2
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author Friston, Karl J.
Parr, Thomas
Zeidman, Peter
Razi, Adeel
Flandin, Guillaume
Daunizeau, Jean
Hulme, Ollie J.
Billig, Alexander J.
Litvak, Vladimir
Moran, Rosalyn J.
Price, Cathy J.
Lambert, Christian
author_facet Friston, Karl J.
Parr, Thomas
Zeidman, Peter
Razi, Adeel
Flandin, Guillaume
Daunizeau, Jean
Hulme, Ollie J.
Billig, Alexander J.
Litvak, Vladimir
Moran, Rosalyn J.
Price, Cathy J.
Lambert, Christian
author_sort Friston, Karl J.
collection PubMed
description 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.
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spelling pubmed-74319772020-08-21 Dynamic causal modelling of COVID-19 Friston, Karl J. Parr, Thomas Zeidman, Peter Razi, Adeel Flandin, Guillaume Daunizeau, Jean Hulme, Ollie J. Billig, Alexander J. Litvak, Vladimir Moran, Rosalyn J. Price, Cathy J. Lambert, Christian Wellcome Open Res Method Article 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. F1000 Research Limited 2020-08-07 /pmc/articles/PMC7431977/ /pubmed/32832701 http://dx.doi.org/10.12688/wellcomeopenres.15881.2 Text en Copyright: © 2020 Friston KJ et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method Article
Friston, Karl J.
Parr, Thomas
Zeidman, Peter
Razi, Adeel
Flandin, Guillaume
Daunizeau, Jean
Hulme, Ollie J.
Billig, Alexander J.
Litvak, Vladimir
Moran, Rosalyn J.
Price, Cathy J.
Lambert, Christian
Dynamic causal modelling of COVID-19
title Dynamic causal modelling of COVID-19
title_full Dynamic causal modelling of COVID-19
title_fullStr Dynamic causal modelling of COVID-19
title_full_unstemmed Dynamic causal modelling of COVID-19
title_short Dynamic causal modelling of COVID-19
title_sort dynamic causal modelling of covid-19
topic Method Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7431977/
https://www.ncbi.nlm.nih.gov/pubmed/32832701
http://dx.doi.org/10.12688/wellcomeopenres.15881.2
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