Coronavirus Disease 2019 has emerged as a significant global concern, triggering harsh public health restrictions in a successful bid to curb its exponential growth. As discussion shifts towards relaxation of these restrictions, there is significant concern of second-wave resurgence. The key to managing these outbreaks is early detection and intervention, and yet there is a significant lag time associated with usage of laboratory confirmed cases for surveillance purposes. To address this, syndromic surveillance can be considered to provide a timelier alternative for first-line screening. Existing syndromic surveillance solutions are however typically focused around a known disease and have limited capability to distinguish between outbreaks of individual diseases sharing similar syndromes. This poses a challenge for surveillance of COVID-19 as its active periods tend to overlap temporally with other influenza-like illnesses. In this study we explore performing sentinel syndromic surveillance for COVID-19 and other influenza-like illnesses using a deep learning-based approach. Our methods are based on aberration detection utilizing autoencoders that leverages symptom prevalence distributions to distinguish outbreaks of two ongoing diseases that share similar syndromes, even if they occur concurrently. We first demonstrate that this approach works for detection of outbreaks of influenza, which has known temporal boundaries. We then demonstrate that the autoencoder can be trained to not alert on known and well-managed influenza-like illnesses such as the common cold and influenza. Finally, we applied our approach to 2019–2020 data in the context of a COVID-19 syndromic surveillance task to demonstrate how implementation of such a system could have provided early warning of an outbreak of a novel influenza-like illness that did not match the symptom prevalence profile of influenza and other known influenza-like illnesses.

2019 年冠状病毒病已成为全球关注的重大问题，引发了严厉的公共卫生限制，成功遏制了其指数级增长。随着讨论转向放宽这些限制，人们对第二波疫情卷土重来感到非常担忧。管理这些疫情的关键是早期发现和干预，但使用实验室确诊病例进行监测存在明显的滞后时间。为了解决这个问题，可以考虑进行症状监测来为一线筛查提供更及时的替代方案。然而，现有的症状监测解决方案通常集中于已知疾病，并且区分具有相似症状的个体疾病的爆发的能力有限。这对 COVID-19 的监测提出了挑战，因为其活跃期往往与其他流感样疾病在时间上重叠。在这项研究中，我们探索使用基于深度学习的方法对 COVID-19 和其他流感样疾病进行哨点综合征监测。我们的方法基于利用自动编码器的畸变检测，该编码器利用症状患病率分布来区分两种具有相似症状的正在进行的疾病的爆发，即使它们同时发生。我们首先证明这种方法适用于检测具有已知时间边界的流感爆发。然后，我们证明自动编码器可以经过训练，使其不对已知且管理良好的流感样疾病（例如普通感冒和流感）发出警报。 最后，我们将我们的方法应用于 COVID-19 综合征监测任务背景下的 2019-2020 年数据，以证明这种系统的实施如何能够对与现有数据不相符的新型流感样疾病的爆发提供早期预警。流感和其他已知流感样疾病的症状流行概况。