Exploring a targeted approach for public health capacity restrictions during COVID-19 using a new computational model

Abstract

This work introduces the Queen's University Agent-Based Outbreak Outcome Model (QUABOOM), a new, data-driven, agent-based Monte Carlo simulation for modelling epidemics and informing public health policy in a wide range of population sizes. We demonstrate how the model can be used to quantitatively inform capacity restrictions for COVID-19 to reduce their impact on small businesses by showing that public health measures should target few locations where many individuals interact rather than many locations where few individuals interact. We introduce a new method for the calculation of the basic reproduction rate that can be applied to low statistics data such as small outbreaks. A novel parameter to quantify the number of interactions in the simulations is introduced which allows our agent-based model to be run using small population sizes and interpreted for larger populations, thereby improving computational efficiency.