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Public perceptions and interactions with UK COVID-19 Test, Trace and Isolate policies, and implications for pandemic infectious disease modelling
Guy C Marshall; Rigina Skeva; Caroline Jay; Miguel E P Silva; Martyn Fyles; Thomas C House; Emma Davis; Li Pi; Graham F Medley; Billy J Quilty; Louise Dyson; Lucy Yardley; Elizabeth Fearon.
Affiliation
  • Guy C Marshall; University of Manchester
  • Rigina Skeva; University of Manchester
  • Caroline Jay; University of Manchester
  • Miguel E P Silva; University of Manchester
  • Martyn Fyles; University of Manchester
  • Thomas C House; University of Manchester
  • Emma Davis; University of Oxford
  • Li Pi; University of Oxford
  • Graham F Medley; London School of Hygiene and Tropical Medicine
  • Billy J Quilty; London School of Hygiene and Tropical Medicine
  • Louise Dyson; University of Warwick
  • Lucy Yardley; University of Bristol
  • Elizabeth Fearon; London School of Hygiene and Tropical Medicine
Preprint in English | medRxiv | ID: ppmedrxiv-22269871
ABSTRACT
The efforts to contain SARS-CoV-2 and reduce the impact of COVID-19 have been supported by Test, Trace and Isolate (TTI) systems in many settings, including the United Kingdom. The mathematical models underlying policy decisions about TTI make assumptions about behaviour in the context of a rapidly unfolding and changeable emergency. This study investigates the reported behaviours of UK citizens in July 2021, assesses them against how a set of TTI processes are conceptualised and represented in models and then interprets the findings with modellers who have been contributing evidence to TTI policy. We report on 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. The study demonstrates the potential of input from members of the public to benefit the modelling process, from guiding the choice 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. We conclude that deeper engagement with members of the public should be integrated at regular stages of public health intervention modelling.
License
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Prognostic study / Rct Language: English Year: 2022 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Experimental_studies / Prognostic study / Rct Language: English Year: 2022 Document type: Preprint
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