A Metric Hybrid Planning Approach to Solving Pandemic Planning Problems with Simple SIR Models
This work addresses pandemic mitigation strategies for public health and policy, but it is incremental as it builds on existing SIR models and planning methods.
The paper tackled pandemic planning by extending the SIR model to include lockdowns and formalizing a metric hybrid planning problem, solving it with a planner enhanced by valid inequalities, resulting in improved runtime effectiveness demonstrated theoretically and experimentally.
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.