Modelling the Spread of COVID-19 in Indoor Spaces using Automated Probabilistic Planning

TL;DR

This paper presents a novel probabilistic planning approach to model and control COVID-19 spread in indoor spaces, enabling simulation of mitigation strategies like masks and capacity limits to inform effective interventions.

Contribution

It introduces a dynamic graph-based probabilistic planning method for COVID-19 spread modeling and intervention design in indoor environments, which is a new application of automated planning techniques.

Findings

Probabilistic planning effectively predicts infection spread in shared indoor spaces.

Automated planners can design interventions to reduce COVID-19 transmission.

Open-source code facilitates further research and application.

Abstract

The coronavirus disease 2019 (COVID-19) pandemic has been ongoing for around 3 years, and has infected over 750 million people and caused over 6 million deaths worldwide at the time of writing. Throughout the pandemic, several strategies for controlling the spread of the disease have been debated by healthcare professionals, government authorities, and international bodies. To anticipate the potential impact of the disease, and to simulate the effectiveness of different mitigation strategies, a robust model of disease spread is needed. In this work, we explore a novel approach based on probabilistic planning and dynamic graph analysis to model the spread of COVID-19 in indoor spaces. We endow the planner with means to control the spread of the disease through non-pharmaceutical interventions (NPIs) such as mandating masks and vaccines, and we compare the impact of crowds and capacity limits on the spread of COVID-19 in these settings. We demonstrate that the use of probabilistic planning is effective in predicting the amount of infections that are likely to occur in shared spaces, and that automated planners have the potential to design competent interventions to limit the spread of the disease. Our code is fully open-source and is available at: https://github.com/mharmanani/prob-planning-covid19 .