Shayegh, Soheil and Andreu-Perez, Javier and Akoth, Caroline and Bosch-Capblanch, Xavier and Dasgupta, Shouro and Falchetta, Giacomo and Gregson, Simon and Hammad, Ahmed T and Herringer, Mark and Kapkea, Festus and Labella, Alvaro and Lisciotto, Luca and Martínez, Luis and Macharia, Peter M and Morales-Ruiz, Paulina and Murage, Njeri and Offeddu, Vittoria and South, Andy and Torbica, Aleksandra and Trentini, Filippo and Melegaro, Alessia (2023) Prioritizing COVID-19 vaccine allocation in resource poor settings:Towards an Artificial Intelligence-enabled and Geospatial-assisted decision support framework. PLoS One, 18 (8). ISSN 1932-6203
Full text not available from this repository.Abstract
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.