Predictions of COVID-19 dynamics in the UK: Short-term forecasting and analysis of potential exit strategies

Abstract

Efforts to suppress transmission of SARS-CoV-2 in the UK have seen non-pharmaceutical interventions being invoked. The most severe measures to date include all restaurants, pubs and cafes being ordered to close on 20th March, followed by a “stay at home” order on the 23rd March and the closure of all non-essential retail outlets for an indefinite period. Government agencies are presently analysing how best to develop an exit strategy from these measures and to determine how the epidemic may progress once measures are lifted. Mathematical models are currently providing short and long term forecasts regarding the future course of the COVID-19 outbreak in the UK to support evidence-based policymaking. We present a deterministic, age-structured transmission model that uses real-time data on confirmed cases requiring hospital care and mortality to provide up-to-date predictions on epidemic spread in ten regions of the UK. The model captures a range of age-dependent heterogeneities, reduced transmission from asymptomatic infections and produces a good fit to the key epidemic features over time. We simulated a suite of scenarios to assess the impact of differing approaches to relaxing social distancing measures from 7th May 2020 on the estimated number of patients requiring inpatient and critical care treatment, and deaths. With regard to future epidemic outcomes, we investigated the impact of reducing compliance, ongoing shielding of elder age groups, reapplying stringent social distancing measures using region based triggers and the role of asymptomatic transmission. We find that significant relaxation of social distancing measures from 7th May onwards can lead to a rapid resurgence of COVID-19 disease and the health system being quickly overwhelmed by a sizeable, second epidemic wave. In all considered age-shielding based strategies, we projected serious demand on critical care resources during the course of the pandemic. The reintroduction and release of strict measures on a regional basis, based on ICU bed occupancy, results in a long epidemic tail, until the second half of 2021, but ensures that the health service is protected by reintroducing social distancing measures for all individuals in a region when required. Our work confirms the effectiveness of stringent non-pharmaceutical measures in March 2020 to suppress the epidemic. It also provides strong evidence to support the need for a cautious, measured approach to relaxation of lockdown measures, to protect the most vulnerable members of society and support the health service through subduing demand on hospital beds, in particular bed occupancy in intensive care units.

A COVID-19 epidemic model predicting the effectiveness of vaccination

“…(2) the cumulative reported cases CR(t) between April 1, 2021 and January 1, 2022 are increasing as the scaling factor ω corresponding to relaxation of social behaviour restrictions increases. These results are consistent with the results in [24] and [34], which were based on extensive data input for the COVID-19 epidemic in the United Kingdom.…”

Modelling SARS-CoV-2 transmission in a UK university setting

“…Our network model results add to similar conclusions drawn from analyses using compartmental models showing that the spread of SARS-CoV-2 within a university student population can be curbed by effective testing, isolation, contract tracing and quarantine [14, 16, 34]. This is also in line with observations outside of a university setting, with the use of nonpharmaceutical interventions to control spread of SARS-CoV-2 at a national scale previously documented [35–37].…”

The impact of school reopening on the spread of COVID-19 in England

“…In order to perform the analysis of school reopening, we extended a previously developed deterministic, age-structured compartmental SARS-CoV-2 transmission model [ 32 ]. The model was matched to a variety of data sources including hospitalizations, ICU occupancy and deaths, while age-dependent parameters were scaled to achieve agreement with the early age-distributions [ 33 ].…”

“…We assessed the school reopening scenarios at a regional scale, modelling the population of England aggregated to seven regions (East of England, London, Midlands, North East and Yorkshire, North West England, South East England, South West England). This involved the use of region-specific posterior parameters obtained in our prior work, where we fit our base transmission model on a region-by-region basis, using a Markov chain Monte Carlo (MCMC) fitting scheme, to four timeseries: (i) new hospitalizations; (ii) hospital bed occupancy; (iii) ICU bed occupancy; and (iv) daily deaths (using data on the recorded date of death, wherever possible) [ 32 ]. The inference was performed from epidemiological data until 12 May 2020.…”