Digital contact tracing apps for COVID, such as the one developed by Google and Apple, need to estimate the risk that a user was infected during a particular exposure, in order to decide whether to notify the user to take precautions, such as entering into quarantine, or requesting a test. Such risk score models contain numerous parameters that must be set by the public health authority. In this paper, we show how to automatically learn these parameters from data. Our method needs access to exposure and outcome data. Although this data is already being collected (in an aggregated, privacy-preserving way) by several health authorities, in this paper we limit ourselves to simulated data, so that we can systematically study the different factors that affect the feasibility of the approach. In particular, we show that the parameters become harder to estimate when there is more missing data (e.g., due to infections which were not recorded by the app), and when there is model misspecification. Nevertheless, the learning approach outperforms a strong manually designed baseline. Furthermore, the learning approach can adapt even when the risk factors of the disease change, e.g., due to the evolution of new variants, or the adoption of vaccines.

翻译：谷歌和苹果开发的COVID数字联系追踪应用软件,例如谷歌和苹果开发的软件,需要估计用户在特定接触期间感染的风险,以便决定是否通知用户采取预防措施,例如检疫,或要求测试。这种风险评分模型包含公共卫生当局必须确定的许多参数。在本文中,我们展示了如何从数据中自动学习这些参数。我们的方法需要获得接触和结果数据。虽然一些卫生当局已经收集了这些数据(以综合、隐私保护的方式),但在本文中,我们仅限于模拟数据,以便我们能够系统地研究影响该方法可行性的不同因素。特别是,我们表明,当缺少更多数据(例如,由于感染,没有在应用程序中记录),以及当模型有误差时,参数就变得更难估计。然而,学习方法超越了一个强有力的手工设计基准。此外,即使疾病风险因素发生变化,例如,由于新的变异体的演变,或疫苗的采用,学习方法也可以适应。