Public perceptions and interactions with UK COVID-19 Test, Trace and Isolate policies, and implications for pandemic infectious disease modelling

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Abstract

Background

The efforts to contain SARS-CoV-2 and reduce the impact of the COVID-19 pandemic have been supported by Test, Trace and Isolate (TTI) systems in many settings, including the United Kingdom. Mathematical models of transmission and TTI interventions, used to inform design and policy choices, make assumptions about the public’s behaviour in the context of a rapidly unfolding and changeable emergency. This study investigates public perceptions and interactions with UK TTI policy in July 2021, assesses them against how TTI processes are conceptualised and represented in models, and then interprets the findings with modellers who have been contributing evidence to TTI policy.

Methods

20 members of the public recruited via social media were interviewed for one hour about their perceptions and interactions with the UK TTI system. Thematic analysis identified key themes, which were then presented back to a workshop of pandemic infectious disease modellers who assessed these findings against assumptions made in TTI intervention modelling. Workshop members co-drafted this report.

Results

Themes included education about SARS-CoV-2, perceived risks, trust, mental health and practical concerns. Findings covered testing practices, including the uses of and trust in different types of testing, and the challenges of testing and isolating faced by different demographic groups. This information was judged as consequential to the modelling process, from guiding the selection of research questions, influencing choice of model structure, informing parameter ranges and validating or challenging assumptions, to highlighting where model assumptions are reasonable or where their poor reflection of practice might lead to uninformative results.

Conclusions

We conclude that deeper engagement with members of the public should be integrated at regular stages of public health intervention modelling.

Article activity feed

  1. SciScore for 10.1101/2022.01.31.22269871: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsConsent: Informed consent was captured through an online booking system and confirmed at the start of each interview.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    NVivo software was used to conduct the thematic analysis [10].
    NVivo
    suggested: (NVivo, RRID:SCR_014802)

    Results from OddPub: Thank you for sharing your code and data.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    In this section, we first reflect on the similarities and differences in the themes our study has identified compared to others, develop our workshop discussions about what these findings mean for how TTI is modelled for informing policy, review the strengths and limitations of our approach and suggest the implications for pandemic transmission and inetrvention response modelling in future. 4.1 Comparison to previous literature about public engagement with TTI and other COVID-19 control policies: Lack of guidance or confusion with government policies related to COVID-19, including but not limited to those relating to TTI, have been reported in other qualitative studies and behavioural surveys. Previous studies of COVID-19 pandemic control measures including social distancing and isolation during March-April 2020, the time of the first ‘lockdown’ in the UK, found similar themes despite the earlier stage of the pandemic [2] and the setting prior to the establishment of NHS Test and Trace in May 2020. Themes emerging from focus groups at this time included loss (social, financial, mental, self-worth); criticism of government communication; adherence, including “High levels of self-adherence but observations of non-adherence in others” which was also identified in our study; and uncertainty. A follow-up study focusing on non-adherence to control policies was conducted in September-November 2020, still nine months prior to ours, and identified some additional themes which again we...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.


    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.