Skip to main content
SpringerLink
Log in

Fighting Reluctance: Engagement, Participation, and Trust

  • Conference paper
  • First Online:
Economics of Grids, Clouds, Systems, and Services (GECON 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 13072))

  • 620 Accesses

Abstract

We propose a multi-round competitive influence maximization model for overcoming vaccination reluctance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Living in a rural area, or being female, are negatively correlated with willingness to be vaccinated, whereas having a higher income and/or more education, are positively correlated, and age is only weakly correlated [6].

  2. 2.

    The COVID-19 vaccination campaign has specific challenges: staggered vaccine deliveries, “vaccine shoppers” (or “vaccine sommeliers” in Brazil [7]) who are picky about which vaccine they take and additional trust issues [3], mixed messages from government agencies over “preferred” vaccines [2], and, of course, the fact that some vaccines require two doses, meaning some of the nudging may need to be repeated. We therefore consider the delivery of each dose as a different vaccination campaign for the sake of this work

  3. 3.

    The objective function is intricate to establish, as there are material costs (supply, infrastructures, workers) as well as less tangible costs (e.g., morbidity and co-morbidity, public support, even political costs). We leave the specifics of such a function to future work since we are concerned here with a specific subproblem: engaging people and nudging them towards participation.

References

  1. Domingos, P., Richardson, M.: Mining the network value of customers. In: ACM SIGKDD Knowledge Discovery and Data Mining, pp. 57–66 (2001)

    Google Scholar 

  2. Gollom, M.: Preferred vaccines message from federal health panel sparks concern, criticism from health experts, Canadian Broadcasting Corporation (CBC), May 5, (2021). https://www.cbc.ca/news/health/vaccine-hesitancy-naci-health-experts-1.6012961

  3. Grant, K.: Why do Canadians think Pfizer is better than Moderna? A look at Covid-19 vaccine shopping, Globe and Mail, June 18 (2021). https://www.theglobeandmail.com/canada/articlewhy-do-canadians-think-pfizer-is-better-than-moderna-a-look-at-covid/

  4. Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: ACM SIGKDD Knowledge Discovery and Data Mining, pp. 137–146 (2003)

    Google Scholar 

  5. Lin, S.C., Lin, S.D., Chen, M.S.: A learning-based framework to handle multi-round multi-party influence maximization on social netowrks. In: ACM SIGKDD Knowledge Discovery and Data Mining (2015)

    Google Scholar 

  6. Loewen, P.: Reaching the vaccine hesitant, Report 5, Public Policy Forum, May 18 (2021). https://ppforum.ca/publications/report-5-reaching-the-vaccine-hesitant/

  7. Phillips, T.: Crackdown on ‘vaccine sommeliers’ as Covid pandemic grips Brazil, Guardian, July 6, (2021). https://www.theguardian.com/world/2021/jul/06/crackdown-on-vaccine-sommeliers-as-covid-pandemic-grips-brazil

  8. Sun, L., Huang, W., Yu, P.S., Chen, W.: Multi-round influence maximization. In: ACM SIGKDD Knowledge Discovery and Data Mining, pp. 2249–2258 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Wilfrid Laurier University, Waterloo, ON, Canada

    A. M. Foley

  2. INRS-EMT, Montréal, QC, Canada

    J.-Ch. Grégoire

Authors
  1. A. M. Foley
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J.-Ch. Grégoire
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J.-Ch. Grégoire .

Editor information

Editors and Affiliations

  1. Harokopio University, Athens, Greece

    Konstantinos Tserpes

  2. Seoul National University, Seoul, Korea (Republic of)

    Jörn Altmann

  3. University of Zaragoza, Zaragoza, Spain

    José Ángel Bañares

  4. Caesarea Rothschild Institute, University of Haifa, Haifa, Israel

    Orna Agmon Ben-Yehuda

  5. University of Leeds, Leeds, UK

    Karim Djemame

  6. University of Ljubljana, Ljubljana, Slovenia

    Vlado Stankovski

  7. Inria Rennes - Bretagne Atlantique Research Centre, Rennes, France

    Bruno Tuffin

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Foley, A.M., Grégoire, JC. (2021). Fighting Reluctance: Engagement, Participation, and Trust. In: Tserpes, K., et al. Economics of Grids, Clouds, Systems, and Services. GECON 2021. Lecture Notes in Computer Science(), vol 13072. Springer, Cham. https://doi.org/10.1007/978-3-030-92916-9_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-92916-9_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-92915-2

  • Online ISBN: 978-3-030-92916-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation