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“Hot-spotting” to improve vaccine allocation by harnessing digital contact tracing technology: An application of percolation theory

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  • Mark D Penney
  • Yigit Yargic
  • Lee Smolin
  • Edward W Thommes
  • Madhur Anand
  • Chris T Bauch

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.

Suggested Citation

  • Mark D Penney & Yigit Yargic & Lee Smolin & Edward W Thommes & Madhur Anand & Chris T Bauch, 2021. "“Hot-spotting” to improve vaccine allocation by harnessing digital contact tracing technology: An application of percolation theory," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-15, September.
    • Handle: RePEc:plo:pone00:0256889
    DOI: 10.1371/journal.pone.0256889
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    2. Serina Chang & Emma Pierson & Pang Wei Koh & Jaline Gerardin & Beth Redbird & David Grusky & Jure Leskovec, 2021. "Mobility network models of COVID-19 explain inequities and inform reopening," Nature, Nature, vol. 589(7840), pages 82-87, January.
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