Goal: The COVID-19 pandemic has emerged as the most severe public health crisis in over a century. As of January 2021, there are more than 100 million cases and 2.1 million deaths. For informed decision making, reliable statistical data and capable simulation tools are needed. Our goal is to develop an epidemic simulator that can model the effects of random population testing and contact tracing. Methods: Our simulator models individuals as particles with the position, velocity, and epidemic status states on a 2D map and runs an SEIR epidemic model with contact tracing and testing modules. The simulator is available on GitHub under the MIT license. Results: The 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%). Conclusions: The Particle-based COVID-19 simulator enables the modeling of intervention measures, random testing, and contact tracing, for epidemic mitigation and suppression.

中文翻译：

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具有接触追踪和测试功能的基于粒子的 COVID-19 模拟器

目标：COVID-19 大流行已成为一个多世纪以来最严重的公共卫生危机。截至 2021 年 1 月，有超过 1 亿例病例和 210 万例死亡。为了做出明智的决策，需要可靠的统计数据和功能强大的模拟工具。我们的目标是开发一种流行病模拟器，可以模拟随机人口测试和接触者追踪的影响。方法：我们的模拟器在二维地图上将个体建模为具有位置、速度和流行状态状态的粒子，并运行带有接触者追踪和测试模块的 SEIR 流行模型。该模拟器可在 GitHub 上获得 MIT 许可。结果： 结果表明，接触者追踪和大规模检测的协同使用有效抑制了疫情（死亡人数减少了72%）。 结论： 基于粒子的 COVID-19 模拟器能够对干预措施、随机测试和接触者追踪进行建模，以缓解和抑制流行病。