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

Shayegh, Soheil, Andreu-perez, Javier, Akoth, Caroline, Bosch-capblanch, Xavier, Dasgupta, Shouro, Falchetta, Giacomo, Gregson, Simon, Hammad, Ahmed T., Herringer, Mark, Kapkea, Festus, Labella, Alvaro, Lisciotto, Luca, Martínez, Luis, Macharia, Peter M., Morales-ruiz, Paulina, Murage, Njeri, Offeddu, Vittoria, South, Andy, Torbica, Aleksandra, 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). e0275037. ISSN 1932-6203

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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.

Item Type:	Article
Additional Information:	© 2023 The Author(s)
Divisions:	LSE Grantham Research Institute
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	15 Aug 2023 14:30
Last Modified:	25 Apr 2024 20:27
URI:	http://eprints.lse.ac.uk/id/eprint/119985

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