Optimizing Real-Time Vaccine Allocation in a Stochastic SIR Model

TL;DR

This paper develops an optimal vaccine allocation strategy in a stochastic SIR model considering resource limitations, delays, and city interactions to minimize epidemic impact.

Contribution

It introduces a method to determine optimal vaccination protocols accounting for stochastic disease spread, resource constraints, and spatial interactions.

Findings

Optimal vaccination strategies depend on resource availability and city coupling.

Tradeoffs between vaccine timing, quantity, and spatial spread are quantified.

The model predicts epidemic size distributions under various intervention scenarios.

Abstract

Real-time vaccination following an outbreak can effectively mitigate the damage caused by an infectious disease. However, in many cases, available resources are insufficient to vaccinate the entire at-risk population, logistics result in delayed vaccine deployment, and the interaction between members of different cities facilitates a wide spatial spread of infection. Limited vaccine, time delays, and interaction (or coupling) of cities lead to tradeoffs that impact the overall magnitude of the epidemic. These tradeoffs mandate investigation of optimal strategies that minimize the severity of the epidemic by prioritizing allocation of vaccine to specific subpopulations. We use an SIR model to describe the disease dynamics of an epidemic which breaks out in one city and spreads to another. We solve a master equation to determine the resulting probability distribution of the final epidemic size. We then identify tradeoffs between vaccine, time delay, and coupling, and we determine the optimal vaccination protocols resulting from these tradeoffs.