Evaluating pooled testing designs in community and healthcare settings for infectious disease management and in particular for epidemics

Testing policies are an essential tool when designing a strategic, coordinated response to an epidemic such as COVID-19. A crucial aspect to consider both when designing and implementing a testing strategy as an intervention is the number of tests available. Given a limited testing capacity, pooled testing (a method that involves combining samples taken from multiple individuals and analysing this with a single diagnostic test) has been suggested to reduce the average number of tests needed to identify infected individuals. Despite this, pooled testing was rarely used during the COVID-19 pandemic due to uncertainty over dilution effects of pooled testing and unfamiliarity of how pooled testing can best be used. Therefore, this thesis aims to bridge this gap by 1) evaluating the impact that pooled testing could have had on disease management including the interplay with other non-pharmaceutical measures, 2) understanding the conditions in the population (such as compliance) and disease dynamics that best suit pooled testing, and 3) designing new pooled testing policies fit for purpose for the contexts studied. To perform these analyses, systematic simulation frameworks are developed. One important aspect that the frameworks developed must include is uncertainty around disease dynamics, as there was during the pandemic. This is why our frameworks use agent-based models comprised of a representative relationship network, disease transmission pathway (an extended SEIR model) and the testing poli- cies to be evaluated. The thesis details the development of these models and how the modelling has been extended as more has become known of SARS-CoV-2 dynamics. The settings of a small town and a ‘typical’ English hospital are considered. In the community setting, pooled testing was found to outperform symptomatic individual testing for a range of metrics (including total and peak infections) when more than a small amount of the population (10%) did not comply with the policy. In the hospital setting, pooled testing was found to have a statistically significant effect on reducing healthcare worker infections. Pooled testing can effectively be incorporated into other measures such as mass-testing schemes and hybrid testing schemes to reduce com- munity infections and testing costs. Whether a policymaker should consider pooled testing in a particular setting depends on a range of factors including viral dynamics, compliance levels and, very importantly, which metrics are of highest priority.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2024

Author dc:creator

• Heath, Bethany

Advisors dc:contributor.advisor

• Robertson, David
• Villar, Sofia

• Pooled Testing
• COVID-19
• Infectious Disease
• Infectious Disease Modelling

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/00eec1c4-9526-46c5-be60-d12a4745e00c/download
• http://purl.org/NET/rdflicense/allrightsreserved

Author Identifier

0009-0009-5126-5424

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/385286