Using the LIST model to Estimate the Effects of Contact Tracing on COVID-19 Endemic Equilibria in England and its Regions

Using the LIST model to Estimate the Effects of Contact Tracing on COVID-19 Endemic Equilibria in England and its Regions

Publication date: Jun 11, 2020

Governments across Europe are preparing for the emergence from lockdown, in phases, to prevent a resurgence in cases of COVID-19. Along with social distancing (SD) measures, contact tracing comprising find, track, trace and isolate (FTTI) policies are also being implemented. Here, we investigate FTTI policies in terms of their impact on the endemic equilibrium. We used a generative model, the dynamic causal ‘Location’, ‘Infection’, ‘Symptom’ and ‘Testing’ (LIST) model to identify testing, tracing, and quarantine requirements. We optimised LIST model parameters based on time series of daily reported cases and deaths of COVID-19 in England and based upon reported cases in the nine regions of England and in all 150 upper tier local authorities. Using these optimised parameters, we forecasted infection rates and the impact of FTTI for each area; national, regional, and local. Predicting data from early June 2020, we find that under conditions of medium-term immunity, a ‘40%’ FTTI policy (or greater), could reach a distinct endemic equilibrium that produces a significantly lower death rate and a decrease in ICU occupancy. Considering regions of England in isolation, some regions could substantially reduce death rates with 20% efficacy. We characterise the accompanying endemic equilibria in terms of dynamical stability, observing bifurcation patterns whereby relatively small increases in FTTI efficacy result in stable states with reduced overall morbidity and mortality. These analyses suggest that FTTI will not only save lives, even if only partially effective, and could underwrite the stability of any endemic steady-state we manage to attain.

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Concepts Keywords
Asymptomatic Pharmaceutical interventions
Australia Variational free energy
Barnsley Hospital services
Bifurcation Parameter Medical specialties
Birmingham Health
Blackburn Epidemiology
Blackpool Infectious diseases
Bolton Epidemics
Bradford Pandemics
Bury RTT
Calderdale Super-spreader
Census Social distancing
Cheshire Influenza
Cheshire East Neuroscience
Cleveland Http
Coronavirus Simulation
County Durham
Coventry
Critical Point
Cumbria
Darlington
Darwen
Death Rate
Derbyshire
Doncaster East
Dynamical Systems Theory
East Midlands
East Midlands Derby
East Yorkshire
Endemic
England
Epidemic
Epidemiological
Equilibrium
Europe
France
Gateshead
H1N1
Halton
Hartlepool
Herefordshire
Hull
Humber
Immunity
Infection
INSERM
Kingston
Kirklees
Knowsley
Lancashire
Leeds
Leicester
Leicestershire
Lincolnshire
Liverpool
Local Authority
Lockdown
London
Manchester
Melbourne
Middlesbrough
Morbidity
Neuroimaging
Neurology
Newcastle
NHS
North East Lincolnshire
North Lincolnshire
North Tyneside
North Yorkshire
Northamptonshire
Northumberland
Oldham
Paris
Population Dynamics
Quarantine
Queen Square
Redcar
Regression
Rhetoric
Rochdale
Rotherham
Salford
Sandwell
SARS
SE5
Sefton
Seroprevalence
Sheffield
Shropshire
Solihull
South Tyneside
Staffordshire
Stockport
Stockton
Stoke
Sunderland
Symptom
Tameside
Tees
Telford
Trafford
Virus
Wakefield
Walsall
Warrington
Warwickshire
West Chester
West Midlands
Wigan
Wirral
Wolverhampton
Worcestershire
Wrekin
Yorkshire

Semantics

Type Source Name
disease MESH infection
disease MESH death
drug DRUGBANK Potassium Chloride
disease MESH community
drug DRUGBANK Spinosad
drug DRUGBANK L-Phenylalanine
drug DRUGBANK Flunarizine
disease MESH bas
disease MESH asymptomatic infections
drug DRUGBANK Angiotensin II
disease MESH pneumonia
disease MESH growth
disease MESH influenza
disease MESH Infectious Diseases
disease MESH Emerging infectious diseases
drug DRUGBANK Carboxyamidotriazole
drug DRUGBANK Coenzyme M

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