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In this paper, we present BESSIE (Behavior and Epidemic Simulator forSynthetic Information Environments), an open source, agent-based simulator forCOVID-type epidemics. BESSIE uses a synthetic population where each person hasdemographic attributes, belong to a household, and has a base activity- andvisit schedule covering seven days. The simulated disease spreads throughcontacts that arise from joint visits to the locations where activities takeplace. The simulation model has a plugin-type programmable behavioral modelwhere, based on the dynamics and observables tracked by the simulator, agentsdecide on actions such as wearing a mask, engaging in social distancing, orrefraining from certain activity types by staying at home instead. The pluginsare supplied as Python code. To the best of our knowledge, BESSIE is a uniquesimulator supporting this feature set, and most certainly as open software.  To illustrate the use of BESSIE, we provide a COVID-relevant exampledemonstrating some of its capabilities. The example uses a synthetic populationfor the City of Charlottesville, Virginia. Both this population and the Pythonplugin modules used in the example are made available. The Pythonimplementation, which can run on anything from a laptop to a cluster, is madeavailable under the Apache 2.0 license(https://www.apache.org/licenses/LICENSE-2.0.html). The example populationaccompanying this publication is made available under the CC BY 4.0 license(https://creativecommons.org/licenses/by/4.0/).

BESSIE: A Behavior and Epidemic Simulator for Use With Synthetic Populations