Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework Article Swipe
Soheil Shayegh
,
Javier Andreu-Pérez
,
Caroline Akoth
,
Xavier Bosch‐Capblanch
,
Shouro Dasgupta
,
Giacomo Falchetta
,
Simon Gregson
,
Ahmed T. Hammad
,
Mark Herringer
,
Festus Kapkea
,
Álvaro Labella
,
Luca Lisciotto
,
Luis Martı́nez
,
Peter M. Macharia
,
Paulina Morales-Ruíz
,
Njeri Murage
,
Vittoria Offeddu
,
Andy South
,
Aleksandra Torbica
,
Filippo Trentini
,
Alessia Melegaro
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0275037
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0275037
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0275037
- OA Status
- gold
- Cited By
- 2
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385727677
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385727677Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0275037Digital Object Identifier
- Title
-
Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support frameworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-10Full publication date if available
- Authors
-
Soheil Shayegh, Javier Andreu-Pérez, Caroline Akoth, Xavier Bosch‐Capblanch, Shouro Dasgupta, Giacomo Falchetta, Simon Gregson, Ahmed T. Hammad, Mark Herringer, Festus Kapkea, Álvaro Labella, Luca Lisciotto, Luis Martı́nez, Peter M. Macharia, Paulina Morales-Ruíz, Njeri Murage, Vittoria Offeddu, Andy South, Aleksandra Torbica, Filippo Trentini, Alessia MelegaroList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0275037Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0275037Direct OA link when available
- Concepts
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Geospatial analysis, Vulnerability (computing), Context (archaeology), Computer science, Vaccination, Environmental health, Medicine, Geography, Immunology, Computer security, Archaeology, CartographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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33Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.comprise | 158 |
| abstract_inverted_index.selected | 91 |
| abstract_inverted_index.showcase | 131 |
| abstract_inverted_index.socially | 155 |
| abstract_inverted_index.strategy | 137, 171 |
| abstract_inverted_index.updating | 48 |
| abstract_inverted_index.vaccines | 41, 119 |
| abstract_inverted_index.according | 42 |
| abstract_inverted_index.comparing | 139 |
| abstract_inverted_index.effective | 115 |
| abstract_inverted_index.factors), | 74 |
| abstract_inverted_index.framework | 5 |
| abstract_inverted_index.including | 59 |
| abstract_inverted_index.mechanism | 46 |
| abstract_inverted_index.strategy, | 154 |
| abstract_inverted_index.vaccines. | 182 |
| abstract_inverted_index.(including | 64 |
| abstract_inverted_index.Artificial | 96 |
| abstract_inverted_index.Objectives | 0 |
| abstract_inverted_index.allocation | 9, 117 |
| abstract_inverted_index.artificial | 28 |
| abstract_inverted_index.collection | 63 |
| abstract_inverted_index.components | 13 |
| abstract_inverted_index.estimation | 79 |
| abstract_inverted_index.evaluating | 32 |
| abstract_inverted_index.geospatial | 24 |
| abstract_inverted_index.introduced | 58 |
| abstract_inverted_index.literature | 104 |
| abstract_inverted_index.scientific | 103 |
| abstract_inverted_index.screening, | 105 |
| abstract_inverted_index.vaccinated | 163 |
| abstract_inverted_index.vulnerable | 156 |
| abstract_inverted_index.Conclusions | 129 |
| abstract_inverted_index.Vaccination | 76 |
| abstract_inverted_index.combination | 22 |
| abstract_inverted_index.considering | 124 |
| abstract_inverted_index.constraints | 126 |
| abstract_inverted_index.elicitation | 100 |
| abstract_inverted_index.healthcare, | 71 |
| abstract_inverted_index.individuals | 157 |
| abstract_inverted_index.objectives. | 128 |
| abstract_inverted_index.performance | 133 |
| abstract_inverted_index.vaccination | 144 |
| abstract_inverted_index.(AI-enabled) | 98 |
| abstract_inverted_index.GIS-assisted | 116 |
| abstract_inverted_index.Intelligence | 97 |
| abstract_inverted_index.Vaccination, | 16 |
| abstract_inverted_index.demographic, | 68 |
| abstract_inverted_index.implemented, | 173 |
| abstract_inverted_index.intelligence | 29 |
| abstract_inverted_index.Vulnerability | 61, 83 |
| abstract_inverted_index.consideration | 109 |
| abstract_inverted_index.environmental | 73 |
| abstract_inverted_index.vulnerability | 33, 49 |
| abstract_inverted_index.Vulnerability, | 15 |
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| abstract_inverted_index.epidemiological, | 70 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5061900944 |
| countries_distinct_count | 8 |
| institutions_distinct_count | 21 |
| corresponding_institution_ids | https://openalex.org/I71209653 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/1 |
| sustainable_development_goals[0].score | 0.699999988079071 |
| sustainable_development_goals[0].display_name | No poverty |
| citation_normalized_percentile.value | 0.68944625 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |