Dynamic causal modelling of COVID-19

Wellcome Open Research

article

Karl J. Friston¹  Thomas Parr¹  Peter Zeidman¹  Adeel Razi¹  Guillaume Flandin¹  Jean Daunizeau³  Ollie J. Hulme⁴  Alexander J. Billig⁶  Vladimir Litvak¹  Rosalyn J. Moran⁷  Cathy J. Price¹  Christian Lambert¹ 

[1] Wellcome Centre for Human Neuroimaging, University College London;Turner Institute for Brain and Mental Health & Monash Biomedical Imaging, Monash University;Institut du Cerveau et de la Moelle épinière;Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre;London Mathematical Laboratory;Ear Institute, University College London;Centre for Neuroimaging Science, Department of Neuroimaging, IoPPN, King's College London

关键词: coronavirus;    epidemiology;    compartmental models;    dynamic causal modelling;    variational;    Bayesian;   

DOI  :  10.12688/wellcomeopenres.15881.2

学科分类：内科医学

来源: Wellcome

【 摘 要 】

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 modelper 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.

【 授权许可】

CC BY