Friston, KJ;
Parr, T;
Zeidman, P;
Razi, A;
Flandin, G;
Daunizeau, J;
Hulme, OJ;
... Lambert, C; + view all
(2021)
Testing and tracking in the UK: A dynamic causal modelling study [version 2; peer review: 1 approved with reservations, 1 not approved].
Wellcome Open Research
, 5
, Article 144. 10.12688/wellcomeopenres.16004.2.
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Abstract
By equipping a previously reported dynamic causal modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a dynamic causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
| Type: | Article |
|---|---|
| Title: | Testing and tracking in the UK: A dynamic causal modelling study [version 2; peer review: 1 approved with reservations, 1 not approved] |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.12688/wellcomeopenres.16004.2 |
| Publisher version: | https://doi.org/10.12688/wellcomeopenres.16004.2 |
| Language: | English |
| Additional information: | This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| 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/10122365 |
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