TY - JOUR

T1 - Prioritizing COVID-19 vaccine allocation in resource poor settings

T2 - 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 M

AU - Morales-Ruiz, Paulina

AU - Murage, Njeri

AU - Offeddu, Vittoria

AU - South, Andy

AU - Torbica, Aleksandra

AU - Trentini, Filippo

AU - Melegaro, Alessia

PY - 2023/8/10

Y1 - 2023/8/10

N2 - To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). 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. 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. 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 - To propose a novel framework for COVID-19 vaccine allocation based on three components of Vulnerability, Vaccination, and Values (3Vs). 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. 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. 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.

KW - COVID-19 - epidemiology - prevention & control

KW - Vaccination

KW - Biological Transport

KW - Artificial Intelligence

KW - COVID-19 Vaccines

KW - Data Analysis

KW - Humans

U2 - 10.1371/journal.pone.0275037

DO - 10.1371/journal.pone.0275037

M3 - Journal article

C2 - 37561732

VL - 18

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 8

M1 - e0275037

ER -