TY - JOUR
T1 - Prioritizing COVID-19 vaccine allocation in resource poor settings: towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework
AU - Shayegh, Soheil
AU - Andreu-Perez, Javier
AU - Akoth, Caroline
AU - Bosch-Capblanch, Xavier
AU - Dasgupta, Shouro
AU - Falchetta, Giacomo
AU - Gregson, Simon
AU - Hammad, Ahmed T
AU - Herringer, Mark
AU - Kapkea, Festus
AU - Labella, Alvaro
AU - Lisciotto, Luca
AU - Martínez, Luis
AU - Macharia, Peter
AU - Morales-Ruiz, Paulina
AU - Murage, Njeri
AU - Offeddu, Vittoria
AU - South, Andy
AU - Torbica, Aleksandra
AU - Trentini, Filippo
AU - Melegaro, Alessia
N1 - FTX; DOAJ; (CC BY 4.0); Copyright: © 2023 Shayegh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1371/journal.pone.0275037
DO - 10.1371/journal.pone.0275037
M3 - A1: Web of Science-article
C2 - 37561732
SN - 1932-6203
VL - 18
JO - PLoS ONE
JF - PLoS ONE
IS - 8
M1 - e0275037
ER -