About: Dynamic causal modelling of COVID-19

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Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Dynamic causal modelling of COVID-19
Creator	Hulme, Oliver Billig, Alexander Daunizeau, Jean Flandin, Guillaume Friston, Karl Lambert, Christian Litvak, Vladimir Moran, Rosalyn Parr, Thomas Price, Cathy Razi, Adeel Zeidman, Peter
Source	ArXiv
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.
has issue date	2020-04-09(xsd:dateTime)
has license	arxiv
sha1sum (hex)	f2b38f1b7e7ec4476ef97dc2df05a2248926828c
resource representing a document's title	Dynamic causal modelling of COVID-19
resource representing a document's body	covid:f2b38f1b7e7ec4476ef97dc2df05a2248926828c#body_text
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