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"Hot-spotting" to improve vaccine allocation by harnessing digital contact tracing technology: An application of percolation theory.
Penney, Mark D; Yargic, Yigit; Smolin, Lee; Thommes, Edward W; Anand, Madhur; Bauch, Chris T.
  • Penney MD; Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada.
  • Yargic Y; Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.
  • Smolin L; Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada.
  • Thommes EW; Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada.
  • Anand M; Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada.
  • Bauch CT; Vaccine Epidemiology and Modeling, Sanofi Pasteur, Toronto, Ontario, Canada.
PLoS One ; 16(9): e0256889, 2021.
Article in English | MEDLINE | ID: covidwho-1523421
ABSTRACT
Vaccinating individuals with more exposure to others can be disproportionately effective, in theory, but identifying these individuals is difficult and has long prevented implementation of such strategies. Here, we propose how the technology underlying digital contact tracing could be harnessed to boost vaccine coverage among these individuals. In order to assess the impact of this "hot-spotting" proposal we model the spread of disease using percolation theory, a collection of analytical techniques from statistical physics. Furthermore, we introduce a novel measure which we call the efficiency, defined as the percentage decrease in the reproduction number per percentage of the population vaccinated. We find that optimal implementations of the proposal can achieve herd immunity with as little as half as many vaccine doses as a non-targeted strategy, and is attractive even for relatively low rates of app usage.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Mass Vaccination / Contact Tracing / COVID-19 Vaccines / COVID-19 Subject: Mass Vaccination / Contact Tracing / COVID-19 Vaccines / COVID-19 Type of study: Risk factors Language: English Journal: PLoS One Year: 2021

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Full text: Available Collection: International databases Database: MEDLINE Document Type: Article Main subject: Mass Vaccination / Contact Tracing / COVID-19 Vaccines / COVID-19 Subject: Mass Vaccination / Contact Tracing / COVID-19 Vaccines / COVID-19 Type of study: Risk factors Language: English Journal: PLoS One Year: 2021
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