Optimal design of vaccination policies: A case study for Newfoundland and Labrador

TL;DR

This paper develops mixed integer programming models for optimizing vaccination policies in Newfoundland and Labrador by minimizing critical network connections, demonstrating improved strategies over real-world approaches during COVID-19.

Contribution

It introduces novel MIP models for the distance-based critical node detection problem and applies them to strategic vaccine allocation in a real-world context.

Findings

2-DCNDP with aggregated inequalities outperforms disaggregated versions for dense graphs

DCNDP-based strategies outperform real-world COVID-19 vaccination strategies

Polyhedral analysis provides new inequalities for the 2-DCNDP

Abstract

This paper proposes pandemic mitigation vaccination policies for Newfoundland and Labrador (NL) based on two compact mixed integer programming (MIP) models of the distance-based critical node detection problem (DCNDP). Our main focus is on two variants of the DCNDP that seek to minimize the number of connections with lengths of at most one (1-DCNDP) and two (2-DCNDP). A polyhedral study for the 1-DCNDP is conducted, and new aggregated inequalities are provided for the 2-DCNDP. The computational experiments show that the 2-DCNDP with aggregated inequalities outperforms the one with disaggregated inequalities for graphs with a density of at least 0.5%. We also study the strategic vaccine allocation problem as a real-world application of the DCNDP and conduct a set of computational experiments on a simulated contact network of NL. Our computational results demonstrate that the DCNDP-based strategies can have a better performance in comparison with the real-world strategies implemented during COVID-19.