Using VERA to explain the impact of social distancing on the spread of COVID-19
This work addresses the need for flexible simulation tools for public health policymakers during the COVID-19 pandemic, but it is incremental as it builds on existing agent-based and SIR modeling approaches.
The authors tackled the problem of modeling COVID-19 spread by developing VERA, an interactive AI tool that allows users to specify conceptual models, run agent-based simulations, and adjust parameters based on data, resulting in a SIR model for COVID-19 and healthcare capacity.
COVID-19 continues to spread across the country and around the world. Current strategies for managing the spread of COVID-19 include social distancing. We present VERA, an interactive AI tool, that first enables users to specify conceptual models of the impact of social distancing on the spread of COVID-19. Then, VERA automatically spawns agent-based simulations from the conceptual models, and, given a data set, automatically fills in the values of the simulation parameters from the data. Next, the user can view the simulation results, and, if needed, revise the simulation parameters and run another experimental trial, or build an alternative conceptual model. We describe the use VERA to develop a SIR model for the spread of COVID-19 and its relationship with healthcare capacity.