BESSIE: A Behavior and Epidemic Simulator for Use With Synthetic Populations (2203.11414v1)

Abstract: In this paper, we present BESSIE (Behavior and Epidemic Simulator for Synthetic Information Environments), an open source, agent-based simulator for COVID-type epidemics. BESSIE uses a synthetic population where each person has demographic attributes, belong to a household, and has a base activity- and visit schedule covering seven days. The simulated disease spreads through contacts that arise from joint visits to the locations where activities take place. The simulation model has a plugin-type programmable behavioral model where, based on the dynamics and observables tracked by the simulator, agents decide on actions such as wearing a mask, engaging in social distancing, or refraining from certain activity types by staying at home instead. The plugins are supplied as Python code. To the best of our knowledge, BESSIE is a unique simulator supporting this feature set, and most certainly as open software. To illustrate the use of BESSIE, we provide a COVID-relevant example demonstrating some of its capabilities. The example uses a synthetic population for the City of Charlottesville, Virginia. Both this population and the Python plugin modules used in the example are made available. The Python implementation, which can run on anything from a laptop to a cluster, is made available under the Apache 2.0 license (https://www.apache.org/licenses/LICENSE-2.0.html). The example population accompanying this publication is made available under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).