Abstract

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

Shayegh, S. Andreu-Perez, J. Akoth, C. Bosch-Capblanch, X. Dasgupta, S. Falchetta, G. Gregson, S. Hammad, A. T. Herringer, M. Kapkea, F. Labella, A. Lisciotto, L. Martinez, L. Macharia, P. M. Morales-Ruiz, P. Murage, N. Offeddu, V. South, A. Torbica, A. Trentini, F. Melegaro, A.
PLoS One. 2023; 18e0275037

Permanent descriptor

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.