Community Testing Review

We need to create a framework for data-driven community testing for future pandemics

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.

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.

Would you like to learn more about the review?

Full report

A summary of the findings of the review can be found below, or click the link to be taken to the full review slides.

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
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