Targeting COVID-19 control measures to optimise economic output while protecting schools, universities and the NHS could benefit UK economy by £193bn.
A new report, from the Imperial College London COVID-19 Response Team in a collaboration between epidemiologists and economists, found that strategically closing higher-risk economic sectors for two-month periods until March 2021, while allowing dozens of other economic sectors to continue, would enable schools and universities to remain open and keep hospitalisations within capacity.
They estimate that this would increase Gross Domestic Product (GDP – a value for all goods and services produced within a country) over six months by an estimated £193 billion (28.8%) compared with a blanket lockdown.
With governments around the world trying to balance economic output against public health in the recent months, the researchers modelled different combinations of closing 63 economic sectors to find which scenario would maximise GDP without letting the pandemic get out of control forcing schools to close and hospitals becoming overwhelmed.
When modelling the potential impact the researchers considered economic sectors with high contact rates and many workers, resulting in high virus transmission, interconnected supply chains, and sectors’ contribution to GDP, while minimising loss in GDP, given different levels of hospital occupancy.
They calculated that a blanket lockdown that maintains only essential services across all 63 economic sectors would allow the education sector to stay open. This might generate an estimated £670 billion in GDP over six months.
The researchers then modelled three scenarios that would enable schools and universities to remain open while increasing GDP over the next six months:
With hospital capacity for the treatment of 12,000 COVID patients, partially closing 14 economic sectors for a set time while opening others might result in an estimated GDP of £833 billion, a gain of £163bn (24.3%) over blanket lockdown.
Allowing no more hospital beds to be occupied by COVID-19 patients than were occupied at the height of the first wave in April 2020 (18,000 patients), partially closing eight economic sectors for a set time while opening others might result in an estimated GDP of £863bn, a gain of £193bn (28.8%).
Capping COVID-19 hospital capacity at 24,000 patients (which would require more hospital beds than were available during the first wave in the UK in April 2020), closing five economic sectors for a set time while opening others will result in an estimated GDP of £875bn, a gain of £205bn (30.6%).
Exploring the balance between the economy and public health, the report includes scenarios with stringent closures for retail, accommodation, food services, as well as the creative arts, entertainment, sports activities, amusement, recreation and personal services, while keeping economic sectors including manufacturing, construction, agriculture and financial services open. This scenario will impact GDP least and prevent transmissions most.
The researchers estimate that a scenario with a fully open economy (with non-pharmaceutical interventions such as social distancing but no sectoral closures) will result in an estimated GDP of £889 billion over six months, but hospitals would exceed capacity and many people would die.
The researchers estimate that a scenario with a fully open economy (with non-pharmaceutical interventions such as social distancing but no sectoral closures) will result in an estimated GDP of up to £889 billion over six months, but hospitals would exceed capacity with around 68,000 COVID-19 patients requiring hospital treatment at the peak in January 2021 (compared to 18,000 covid-19 patients requiring treatment in April 2020, more than 3 times the number of COVID-19 patients).
The scenario’s presented in the report rely on the assumption that other non-pharmaceutical interventions, such as social distancing, testing, self-isolation, mask wearing and handwashing are stringent. The recommended priority list of sectors to keep open and close proved robust to extensive sensitivity analyses.
