Particle-Based COVID-19 Simulator with Contact Tracing and Testing

GoalCOVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of December 2020, there are more than 60 million cases and 1.4 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator which can model the effects of random population testing and contact tracing. MethodsOur simulator models each individuals as particles with position, velocity and epidemic status states on a 2D map and runs a SEIR epidemic model with contact tracing and testing modules. The simulator is available in GitHub under MIT license. ResultsThe results show that the synergistic use of contact tracing and massive testing is effective in suppressing the epidemic (the number of deaths was reduced by 72%). ConclusionsParticle-based COVID-19 simulator enables the modeling of intervention measures, random testing and contact tracing, for epidemic mitigation and suppression. Impact StatementOur particle-based epidemic simulator calibrated with COVID-19 data models each individual as a unique particle with location, velocity and epidemic state, enabling consideration of contact tracing and testing measures.