A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models

TL;DR

This paper introduces a novel hybrid planning approach that extends simple SIR models to include lockdowns, enabling more effective pandemic mitigation strategies through improved computational methods.

Contribution

It formalizes a metric hybrid planning problem based on extended SIR models and enhances planning efficiency with valid inequalities, demonstrating both theoretical and experimental success.

Findings

Improved runtime efficiency of the hybrid planner.

Successful application to various challenging pandemic scenarios.

Theoretical validation of the planning approach.

Abstract

A pandemic is the spread of a disease across large regions, and can have devastating costs to the society in terms of health, economic and social. As such, the study of effective pandemic mitigation strategies can yield significant positive impact on the society. A pandemic can be mathematically described using a compartmental model, such as the Susceptible Infected Removed (SIR) model. In this paper, we extend the solution equations of the SIR model to a state transition model with lockdowns. We formalize a metric hybrid planning problem based on this state transition model, and solve it using a metric hybrid planner. We improve the runtime effectiveness of the metric hybrid planner with the addition of valid inequalities, and demonstrate the success of our approach both theoretically and experimentally under various challenging settings.