Friston, KJ; Parr, T; Zeidman, P; Razi, A; Flandin, G; Daunizeau, J; Hulme, OJ; ... Lambert, C; + view all Friston, KJ; Parr, T; Zeidman, P; Razi, A; Flandin, G; Daunizeau, J; Hulme, OJ; Billig, AJ; Litvak, V; Moran, RJ; Price, CJ; Lambert, C; - view fewer (2020) Dynamic causal modelling of COVID-19 [version 1; peer review: 1 approved, 1 not approved]. Wellcome Open Research , 5 , Article 89. 10.12688/wellcomeopenres.16253.1.

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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.

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