Your browser doesn't support javascript.
Modelling digital and manual contact tracing for COVID-19. Are low uptakes and missed contacts deal-breakers?
Rusu, Andrei C; Emonet, Rémi; Farrahi, Katayoun.
  • Rusu AC; Vision, Learning and Control Research Group, University of Southampton, Southampton, United Kingdom.
  • Emonet R; Department of Machine Learning, Laboratoire Hubert Curien, Saint-Etienne, France.
  • Farrahi K; Vision, Learning and Control Research Group, University of Southampton, Southampton, United Kingdom.
PLoS One ; 16(11): e0259969, 2021.
Article in English | MEDLINE | ID: covidwho-1523443
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
Comprehensive testing schemes, followed by adequate contact tracing and isolation, represent the best public health interventions we can employ to reduce the impact of an ongoing epidemic when no or limited vaccine supplies are available and the implications of a full lockdown are to be avoided. However, the process of tracing can prove feckless for highly-contagious viruses such as SARS-CoV-2. The interview-based approaches often miss contacts and involve significant delays, while digital solutions can suffer from insufficient adoption rates or inadequate usage patterns. Here we present a novel way of modelling different contact tracing strategies, using a generalized multi-site mean-field model, which can naturally assess the impact of manual and digital approaches alike. Our methodology can readily be applied to any compartmental formulation, thus enabling the study of more complex pathogen dynamics. We use this technique to simulate a newly-defined epidemiological model, SEIR-T, and show that, given the right conditions, tracing in a COVID-19 epidemic can be effective even when digital uptakes are sub-optimal or interviewers miss a fair proportion of the contacts.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks / Models, Statistical / Contact Tracing / COVID-19 Type of study: Observational study Topics: Vaccines Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pone.0259969

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Disease Outbreaks / Models, Statistical / Contact Tracing / COVID-19 Type of study: Observational study Topics: Vaccines Limits: Humans Language: English Journal: PLoS One Journal subject: Science / Medicine Year: 2021 Document Type: Article Affiliation country: Journal.pone.0259969