Friston, KJ;
Parr, T;
Zeidman, P;
Razi, A;
Flandin, G;
Daunizeau, J;
Hulme, OJ;
... Lambert, C; + view all
(2020)
Dynamic causal modelling of COVID-19 [version 2; peer review: 2 approved].
Wellcome Open Research
, 5
p. 89.
10.12688/wellcomeopenres.15881.2.
Preview |
Text
eec8a7f4-ed85-4fc0-b862-ff9a64ff79fc_15881_-_adeel_razi_v2.pdf - Published Version Download (3MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Dynamic causal modelling of COVID-19 [version 2; peer review: 2 approved] |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.12688/wellcomeopenres.15881.2 |
| Publisher version: | https://doi.org/10.12688/wellcomeopenres.15881.2 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | coronavirus, epidemiology, compartmental models, dynamic causal modelling, variational, Bayesian |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > The Ear Institute UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10108158 |
Archive Staff Only
 |
View Item |
