With more than 1.7 million COVID-19 deaths, identifying effective measures to prevent COVID-19 is a top priority. We developed a mathematical model to simulate the COVID-19 pandemic with digital contact tracing and testing strategies. The model uses a real-world social network generated from a high-resolution contact data set of 180 students. This model incorporates infectivity variations, test sensitivities, incubation period, and asymptomatic cases. We present a method to extend the weighted temporal social network and present simulations on a network of 5000 students. The purpose of this work is to investigate optimal quarantine rules and testing strategies with digital contact tracing. The results show that the traditional strategy of quarantining direct contacts reduces infections by less than 20% without sufficient testing. Periodic testing every 2 weeks without contact tracing reduces infections by less than 3%. A variety of strategies are discussed including testing second and third degree contacts and the pre-exposure notification system, which acts as a social radar warning users how far they are from COVID-19. The most effective strategy discussed in this work was combining the pre-exposure notification system with testing second and third degree contacts. This strategy reduces infections by 18.3% when 30% of the population uses the app, 45.2% when 50% of the population uses the app, 72.1% when 70% of the population uses the app, and 86.8% when 95% of the population uses the app. When simulating the model on an extended network of 5000 students, the results are similar with the contact tracing app reducing infections by up to 79%.

超过 170 万人因 COVID-19 死亡，确定预防 COVID-19 的有效措施是重中之重。我们开发了一个数学模型，通过数字接触者追踪和测试策略来模拟 COVID-19 大流行。该模型使用由 180 名学生的高分辨率联系人数据集生成的真实世界社交网络。该模型结合了传染性变异、测试敏感性、潜伏期和无症状病例。我们提出了一种扩展加权时间社交网络的方法，并在 5000 名学生的网络上进行了模拟。这项工作的目的是通过数字联系人追踪来研究最佳隔离规则和测试策略。结果表明，在没有充分检测的情况下，隔离直接接触者的传统策略将感染率降低了不到 20%。在不追踪接触者的情况下每 2 周进行一次定期检测可将感染率降低不到 3%。讨论了各种策略，包括测试二度和三度接触者以及暴露前通知系统，该系统充当社交雷达，警告用户他们与 COVID-19 的距离。这项工作中讨论的最有效策略是将暴露前通知系统与测试二级和三级接触者相结合。当 30% 的人口使用该应用程序时，该策略可将感染率降低 18.3%，当 50% 的人口使用该应用程序时，感染率降低 45.2%，当 70% 的人口使用该应用程序时，感染率降低 72.1%，当 95% 的人口使用该应用程序时，感染率降低 86.8%使用该应用程序。在 5000 名学生的扩展网络上模拟模型时，结果与接触者追踪应用程序相似，可减少高达 79% 的感染。