A Visual Analytics and Multi-Objective Optimisation Approach for Balancing Economic and Public Health Objectives through Compartmental Models

Modelling of disease spread continues to play a crucial role in the response to the COVID-19 pandemic. There is light at the end of the tunnel with effective vaccines, but it will take until the summer of 2021 for distribution to be widespread, and re-vaccination may be an ongoing requirement. In the meantime, hybrid solutions are required to manage non-pharmaceutical interventions (NPI), that minimise the restrictions to our daily lives while suppressing transmission and maintaining the integrity of our healthcare systems. Realistic models are publicly available to predict the spread of the virus. Varying the parameters of these models can be used to represent tentative policy actions, and the consequences are deduced in simulations of the model. Typically, the main objective for identifying effective policy actions has been to reduce the infection rate. However, often there are multiple, potentially conflicting, objectives that require optimisation in parallel. For instance, we may want policies that reduce the hospital occupancy, while simultaneously reducing economic impacts. Our goal is to provide a generic visual analytics framework to explore the parameter space of complex models as well as the trade-offs between objectives to inform policy makers. Specifically: 1. A scalable visual analytics framework for parameter space exploration of feasible regions of the parameter space for complex compartmental models in order to identify effective policy actions. 2. Extend this framework to handle multiple objectives: reduction of transmission to high risk groups, overall cases and deaths, hospital costs, thresholds for circuit breakers, and economic factors.