Understanding the effect of contact tracing on the Covid-19 pandemic

Understanding the effect of contact tracing on the Covid-19 pandemic

This research used data made available via the Office for National Statistics (ONS) Secure Research Service, which is being expanded and improved with ADR UK funding.

Authors: Dr. Emma Louise Davis (University of Oxford), Elizabeth Fearon (London School of Tropical Medicine and Hygiene), The University of Manchester and the Independent Scientific Pandemic Insights Group on Behaviours

Date: May to September 2021

Research summary

Collaborative research using secure NHS Test & Trace data evaluated the performance of UK contact tracing efforts during the Covid-19 (SARS-CoV2) pandemic. This contributed to reports that were presented to the Scientific Pandemic Influenza Group on Modelling, some of which were passed to the Scientific Advisory Group for Emergencies (SAGE). The reports were used as evidence to support policy and public health decision-making across the pandemic.

The research looked at:

  • The value of backwards contact tracing (identifying and managing individuals who had been exposed to someone with Covid-19)
  • Trade-offs between isolation period and adherence to isolation rules
  • Use of lateral flow tests and testing resource allocation
  • Daily testing policies and post-lockdown scenarios.

The policy-led enquiry emerged from SAGE’s interest in backwards contact tracing and the legal mandating of isolation. The work was guided through partnerships with NHS Test and Trace, UK Health Security Agency, the Department of Health and Social Care, and discussions with the Scientific Pandemic Influenza Group on Modelling around Test, Trace and Isolate.

Data used

This project accessed secure NHS Test & Trace data between March 2021 and May 2022 through the ONS Secure Research Service.

An additional data source used was CoMIX reports.

Methods used

Behavioural scientists evaluated the importance of isolation adherence behaviours to the efficacy of contact tracing in the UK. Isolation, or self-isolation, was defined as individuals who had tested positive for or been exposed to Covid-19 staying home for a set duration.

The innovative research was grounded in mathematical modelling. This was specifically the construction of a branching process model of transmission, combined with a probabilistic framework simulating a variety of test, trace and isolate policies.

Branching process models can be used to simulate a transmission network by considering infection in terms of generations, where each infectious individual in a particular generation infects some number of new people. These people then become infectious in the subsequent generation. This number is randomly sampled according to a distribution defined by the R number (the average number of cases caused by one infectious individual) and the degree of clustering or “super-spreading”.

For each generation, cases could be detected through either contact tracing from their infector in the previous generation, self-reporting of symptoms, or testing - otherwise a case was considered undetected. Once a case was detected they were considered to be isolated, with some probability of adherence to isolation, and some proportion of their contacts able to be traced.

Contact tracing datasets informed the selection of parameters for this model, including parameters for the proportion and number of contacts successfully traced and testing assumptions. These datasets were also used to summarise and visualise temporal (time-related) and regional trends in important factors, such as the delay to receiving test results, the number of reported contacts (as a proxy for levels of social mixing), and the proportion of contacts testing positive. These were used to inform changes in model structure across the pandemic.

Model results were compared with results from another test, trace and isolate model to examine the effects of households and improve the robustness of findings.

Research findings

The investigations around public adherence to isolation, self-reporting of symptoms, and contact reporting found that adherence was likely to be the most important predictor of programme impact. Reports of poor adherence could therefore go some way to explain the gap between the expected impact of contact tracing at the start of the pandemic and the true impact.

Whilst contact tracing was not appropriate as a sole control measure, well implemented contact tracing could provide up to a 15% reduction in the R number if adherence could be improved (see Figure). This would be done by enhancing public messaging or increasing the provision of financial and logistical support.

Building on these findings, the potential effects of legally-mandated isolation were examined. Policies that increased the probability of people self-isolating or the average duration of self-isolation were found not to substantially decrease the risk of a large outbreak. The usefulness of self-administered lateral flow tests within the contact tracing framework was also considered. While the test sensitivity of lateral flow tests was only around 65%, compared with 95% for polymerase chain reaction test, findings suggested that optimizing self-isolation rates over test-sensitivity minimizes risk of spread.

Research impact

The project resulted in more than ten separate reports presented to the Scientific Pandemic Influenza Group on Modelling, on a range of topics relating to contact tracing. Five of these were passed to SAGE.  The reports were used as evidence to support policy and public health decision making across the pandemic.

It was agreed that overall, policies that improve self-report rates, even at the expense of self-isolation rates should be used. This includes publicity that encourages people to self-report.

A policy to analyse daily contact testing methods was implemented in the UK in December 2021 and ran until mid-February 2022, with free lateral flow tests available until the end of March 2022.  Investigations of the potential positive effect on adherence to isolation reduced the mandated isolation period for contacts of Covid-19 cases; in December 2021 UK policy changed from 10 days to 7 days , and reduced again in January 2022 to 5 days.

The research team also contributed to collaborative meetings with NHS Test and Trace, the Department of Health and Social Care, and UK Health Security Agency. This included reviewing and providing input into the NHS Test and Trace effectiveness model.

This work fed into discussions around reactive modelling during an emerging situation, such as a pandemic. They particularly focused on a lack of evidence around population behaviours in the context of rapidly changing policy and public health messaging. The discussions resulted in a publication on the interaction between contact tracing policy and public perceptions, and the implications of this for infectious disease modelling and forecasting.

The research team has published several open-access peer-reviewed articles detailing their findings, contributing to the wider body of Covid-19 research. The project gained media coverage, with researchers interviewed about this work by BBC Radio 4, Times Radio, and the BBC News Channel from March 2021 to December 2021.

Finally, the work helped consolidate and record lessons learned across the pandemic period, with a view to informing future public health policy.

Research outputs

Publications and reports

Presentations and awards

About the ONS Secure Research Service

The ONS Secure Research Service is an accredited trusted research environment, using the Five Safes Framework to provide secure access to de-identified, unpublished data. If you use ONS SRS data and would like to discuss writing a future case study with us, please ensure you have reported your outputs here: Outputs Reporting Form

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