Large-scale community testing was piloted in Liverpool during the COVID-19 pandemic, with the hope that it would help with the reopening of activities key to society and the economy, while controlling transmission of the virus. It is a massively complex intervention that needs more data, real-time intelligence and background understanding to deploy for maximum value. This review seeks to better understand how we might implement and exit future community testing schemes.
We need to create a framework for data-driven community testing for future pandemics
Large-scale testing enabled us to:
- Protect vulnerable individuals and settings
- Release people from quarantine or isolation
- Have a safer return to activities key to society and the economy
Evaluation found that the mass testing was associated with an overall 25% reduction in COVID-19 related hospital admissions.
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Full report
About the review
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Our objectives
Convene a board of experts involved in the mass community testing to:
1. Reflect on Covid-19 community testing evaluations / lessons
2. Identify data and evaluation requirements for intelligence-led (de)escalation of community testing in future scenarios
3. Publish a framework for programmable risk-mitigation with intelligence-led community testing in pandemics / epidemics
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What we found
Areas for further development:
– Science communications
– Rapid spread of evidence and best practice
– Coproduction of tests with users
– Understanding testing motivation
– Complex intervention evidence
– Use of AIs for behavioural insights
– Access to national data
– Timely intelligence on viral kinetics
– Economic models
– Systems thinking – ‘national grid’ of local public health -
Our recommendations
For future community testing schemes we recommend:
– Prompt, well-governed access to nationwide data
– Prepare for AIs that can augment complex decision-making and (semi-)automate responses
– Prepare the policy, political and public health system for distributed resilience to pandemics
– Define strategies for exiting large-scale/high-cost testing promptly
– Set proportionate evaluation policies for testing programmes
– Prepare sleeper protocols now
– Prepare to work more with security services via DSTL
– Rehearse different behavioural scenarios around testing uptake/hesitancy and test result reporting
– Optimise self-test kit usability and reporting functionality now
– Pre-engineer companion AIs to optimise testing value for individuals and society
– Prepare antiviral policies linked to testing policies
– Prepare to link science and industrial supply chains flexibly
– Incentivise supply chain agility through HealthTech industry growth
Community testing film
This film looks at the Liverpool mass community testing scheme during the Covid-19 pandemic, and explores how we can use data to respond in future pandemics.