Manning College of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA, United States.
Halıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States.
Front Public Health. 2024 Mar 28;12:1279392. doi: 10.3389/fpubh.2024.1279392. eCollection 2024.
Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform () in the emergency room of a large hospital. The platform captured syndromic signals from audio, thermal imaging, and radar, while the ground truth data were collected from the hospital's electronic health record. Our analysis revealed a significant correlation between the aggregated cough count and positive COVID-19 cases in the hospital (Pearson correlation of 0.40, value < 0.001). Notably, this correlation was higher than that observed with the number of individuals presenting with fever (ρ = 0.22, = 0.04), a widely used syndromic signal and screening tool for such diseases. Furthermore, we demonstrate how the data obtained from our platform could be leveraged to estimate various COVID-19-related statistics using multiple modeling approaches. Aggregated cough counts and other data, such as people density collected from our platform, can be utilized to predict COVID-19 patient visits related metrics in a hospital waiting room, and SHAP and Gini feature importance-based metrics showed cough count as the important feature for these prediction models. Furthermore, we have shown that predictions based on cough counting outperform models based on fever detection (e.g., temperatures over 39°C), which require more intrusive engagement with the population. Our findings highlight that incorporating cough-counting based signals into syndromic surveillance systems can significantly enhance overall resilience against future public health challenges, such as emerging disease outbreaks or pandemics.
症状监测是一种有效的工具，可实现及时发现传染病暴发，并为公共卫生当局实施有效的缓解策略提供便利。虽然目前利用各种信息来源来收集分析症状信号数据，但咳嗽这一重要症状的综合测量并未广泛用作症状信号。随着普及传感技术的最新进展，以非接触式、不引人注意且自动化的方式持续测量人群水平的咳嗽发生率成为可能。在这项工作中，我们证明了监测综合咳嗽计数作为一种综合征指标来估计 COVID-19 病例的效用。在我们的研究中，我们在一家大医院的急诊室部署了一个基于传感器的平台（）。该平台从音频、热成像和雷达捕获症状信号，而从医院的电子健康记录中收集真实数据。我们的分析表明，医院内的综合咳嗽计数与 COVID-19 阳性病例之间存在显著相关性（医院内的相关性为 0.40，值 < 0.001）。值得注意的是，与出现发热的个体数量（ρ=0.22，值=0.04）相比，这种相关性更高，发热是一种广泛用于此类疾病的症状信号和筛查工具。此外，我们展示了如何利用我们的平台获得的数据，使用多种建模方法来估计各种 COVID-19 相关统计数据。综合咳嗽计数和其他数据，如从我们的平台收集的人群密度，可用于预测医院候诊室中与 COVID-19 患者就诊相关的指标，SHAP 和基尼特征重要性指标表明咳嗽计数是这些预测模型的重要特征。此外，我们已经表明，基于咳嗽计数的预测优于基于发烧检测的模型（例如，体温超过 39°C），后者需要更深入地接触人群。我们的研究结果表明，将基于咳嗽计数的信号纳入症状监测系统可以显著提高整体应对未来公共卫生挑战的能力，例如新发疾病暴发或大流行。
