Validating Optimal COVID-19 Vaccine Distribution Models

TL;DR

This paper presents a clustering and constraint satisfaction approach to optimize COVID-19 vaccine distribution, focusing on selecting centers and allocating vaccines efficiently based on priority and distance, demonstrated with real-world data from Chennai.

Contribution

It introduces a novel combination of clustering and CSP models for vaccine distribution, considering demographic and logistical factors, validated with real-world data.

Findings

Optimal distribution centers identified in Chennai

Efficient vaccine allocation across demographics

Flexible model accommodating various priorities

Abstract

With the approval of vaccines for the coronavirus disease by many countries worldwide, most developed nations have begun, and developing nations are gearing up for the vaccination process. This has created an urgent need to provide a solution to optimally distribute the available vaccines once they are received by the authorities. In this paper, we propose a clustering-based solution to select optimal distribution centers and a Constraint Satisfaction Problem framework to optimally distribute the vaccines taking into consideration two factors namely priority and distance. We demonstrate the efficiency of the proposed models using real-world data obtained from the district of Chennai, India. The model provides the decision making authorities with optimal distribution centers across the district and the optimal allocation of individuals across these distribution centers with the flexibility to accommodate a wide range of demographics.