Prioritizing COVID-19 vaccine allocation in resource poor settings: Towards an Artificial Intelligence-enabled and...

Area of Science:

• Public Health
• Epidemiology
• Health Informatics

Background:

• Equitable COVID-19 vaccine allocation remains a global challenge.
• Current strategies may not adequately address social vulnerability.
• Optimizing vaccine distribution is crucial for pandemic control.

Purpose of the Study:

• To propose a novel framework for COVID-19 vaccine allocation using Vulnerability, Vaccination, and Values (3Vs).
• To develop a dynamic mechanism for updating vulnerability and vaccine uptake.
• To ensure equitable distribution prioritizing vulnerable populations.

Main Methods:

• Geospatial data analysis and artificial intelligence (AI) methods.
• Vulnerability data collection (demographic, socioeconomic, epidemiological, healthcare, environmental).
• AI-enabled expert elicitation for Vulnerability Index creation.
• GIS-assisted allocation considering local constraints.

Main Results:

• A comprehensive 3Vs framework integrating vulnerability, vaccination status, and societal values.
• An AI-driven approach to estimate a unique Vulnerability Index.
• Demonstration of improved equity in vaccine allocation compared to current methods.

Conclusions:

• The 3Vs strategy significantly enhances vaccine prioritization for socially vulnerable groups.
• Implementation of the 3Vs framework could lead to more equitable COVID-19 vaccine distribution.
• The proposed framework offers a dynamic and context-specific solution for vaccine allocation.