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

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

doi:10.1371/journal.pone.0256889

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

PLoS One. 2021 Sep 22;16(9):e0256889. doi: 10.1371/journal.pone.0256889. eCollection 2021.

Authors

Mark D Penney^{1

2}, Yigit Yargic^{1

3}, Lee Smolin¹, Edward W Thommes^{4

5

6}, Madhur Anand⁷, Chris T Bauch²

Affiliations

¹ Perimeter Institute for Theoretical Physics, Waterloo, Ontario, Canada.
² Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.
³ Department of Physics and Astronomy, University of Waterloo, Waterloo, Ontario, Canada.
⁴ Vaccine Epidemiology and Modeling, Sanofi Pasteur, Toronto, Ontario, Canada.
⁵ Department of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada.
⁶ Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada.
⁷ School of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada.

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

COVID-19 / immunology
COVID-19 / prevention & control*
COVID-19 / transmission*
COVID-19 Vaccines / administration & dosage*
Contact Tracing / instrumentation
Contact Tracing / statistics & numerical data*
Humans
Immunity, Herd
Mass Vaccination / statistics & numerical data*
Mobile Applications
Models, Statistical
SARS-CoV-2 / pathogenicity

Substances

COVID-19 Vaccines

Grants and funding

This work was supported by the NSERC COVID-19 Alliance Grant ALLRP 554380- 20 (MDP, MA, CTB) and the Ontario Ministry of Colleges and Universities COVID19 Rapid Response Grant C-299-137-BAUCH (MA, CTB). The NSERC COVID19 Alliance Grant was held in partnership with Sanofi Pasteur who made only a contribution in-kind in the form of their employee (EWT). The funders provided support in the form of salaries for authors (MDP, EWT), but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.