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

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 accordin...

Full description

Saved in:

Bibliographic Details
Published in:	PloS one Vol. 18; no. 8; p. e0275037
Main Authors:	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, Melegaro, Alessia
Format:	Journal Article
Language:	English
Published:	United States Public Library of Science 10-08-2023 Public Library of Science (PLoS)
Subjects:	Analysis Artificial Intelligence Biological Transport Biology and Life Sciences Computer and Information Sciences COVID-19 - epidemiology COVID-19 - prevention & control COVID-19 Vaccines Data Analysis Data collection Data entry Decision making Environmental factors Epidemiology Geospatial data Health facilities Health risks Humans Immunization Infections Information management Medicine and Health Sciences Pandemics People and Places Population Public health Resource allocation Sociodemographics Socioeconomic factors Spatial data Surveys Vaccination Vaccines Italy Kenya Sub-Saharan Africa Africa
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 SS and AM are joint senior authors and contributed equally to this work Competing Interests: The authors have declared that no competing interests exist.
ISSN:	1932-6203 1932-6203
DOI:	10.1371/journal.pone.0275037