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Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework

Author

Listed:
  • Soheil Shayegh
  • Javier Andreu-Perez
  • Caroline Akoth
  • Xavier Bosch-Capblanch
  • Shouro Dasgupta
  • Giacomo Falchetta
  • Simon Gregson
  • Ahmed T Hammad
  • Mark Herringer
  • Festus Kapkea
  • Alvaro Labella
  • Luca Lisciotto
  • Luis Martínez
  • Peter M Macharia
  • Paulina Morales-Ruiz
  • Njeri Murage
  • Vittoria Offeddu
  • Andy South
  • Aleksandra Torbica
  • Filippo Trentini
  • Alessia Melegaro

Abstract

Objectives: To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). Methods: A combination of geospatial data analysis and artificial intelligence methods for evaluating vulnerability factors at the local level and allocate vaccines according to a dynamic mechanism for updating vulnerability and vaccine uptake. Results: A novel approach is introduced including (I) Vulnerability data collection (including country-specific data on demographic, socioeconomic, epidemiological, healthcare, and environmental factors), (II) Vaccination prioritization through estimation of a unique Vulnerability Index composed of a range of factors selected and weighed through an Artificial Intelligence (AI-enabled) expert elicitation survey and scientific literature screening, and (III) Values consideration by identification of the most effective GIS-assisted allocation of vaccines at the local level, considering context-specific constraints and objectives. Conclusions: We showcase the performance of the 3Vs strategy by comparing it to the actual vaccination rollout in Kenya. We show that under the current strategy, socially vulnerable individuals comprise only 45% of all vaccinated people in Kenya while if the 3Vs strategy was implemented, this group would be the first to receive vaccines.

Suggested Citation

  • Soheil Shayegh & Javier Andreu-Perez & Caroline Akoth & Xavier Bosch-Capblanch & Shouro Dasgupta & Giacomo Falchetta & Simon Gregson & Ahmed T Hammad & Mark Herringer & Festus Kapkea & Alvaro Labella , 2023. "Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-11, August.
    • Handle: RePEc:plo:pone00:0275037
    DOI: 10.1371/journal.pone.0275037
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    References listed on IDEAS

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    1. repec:plo:pone00:0231866 is not listed on IDEAS
    2. Salomón Wollenstein-Betech & Amanda A B Silva & Julia L Fleck & Christos G Cassandras & Ioannis Ch Paschalidis, 2020. "Physiological and socioeconomic characteristics predict COVID-19 mortality and resource utilization in Brazil," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    3. Solís Arce, Julio S. & Warren, Shana S. & Meriggi, Niccolò F. & Scacco, Alexandra & McMurry, Nina & Voors, Maarten & Syunyaev, Georgiy & Malik, Amyn Abdul & Aboutajdine, Samya & Adeojo, Opeyemi & Anig, 2021. "COVID-19 vaccine acceptance and hesitancy in low- and middle-income countries," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 27, pages 1-1.
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