One of the main problems in controlling COVID-19 epidemic spread is the delay in confirming cases. Having information on changes in the epidemic evolution or outbreaks rise before laboratory-confirmation is crucial in decision making for Public Health policies. We present an algorithm to estimate on-stream the number of COVID-19 cases using the data from telephone calls to a COVID-line. By modelling the calls as background (proportional to population) plus signal (proportional to infected), we fit the calls in Province of Buenos Aires (Argentina) with coefficient of determination R² > 0.85. This result allows us to estimate the number of cases given the number of calls from a specific district, days before the laboratory results are available. We validate the algorithm with real data. We show how to use the algorithm to track on-stream the epidemic, and present the Early Outbreak Alarm to detect outbreaks in advance of laboratory results. One key point in the developed algorithm is a detailed track of the uncertainties in the estimations, since the alarm uses the significance of the observables as a main indicator to detect an anomaly. We present the details of the explicit example in Villa Azul (Quilmes) where this tool resulted crucial to control an outbreak on time. The presented tools have been designed in urgency with the available data at the time of the development, and therefore have their limitations which we describe and discuss. We consider possible improvements on the tools, many of which are currently under development.

控制COVID-19流行病传播的主要问题之一是确诊病例的延迟。在实验室确认之前获得有关流行病演变或暴发的变化的信息对于公共卫生政策的决策至关重要。我们提出了一种算法，可以使用从电话到COVID线路的数据来估算在流方式上的COVID-19病例数。通过将调用建模为背景（与人口成比例）加上信号（与感染成比例），我们将布宜诺斯艾利斯省（阿根廷）的调用与确定系数R ²拟合。> 0.85。该结果使我们能够在实验室结果可用的几天前，根据特定地区的电话数量来估计病例数。我们用真实数据验证该算法。我们将展示如何使用该算法跟踪流行病的流行，并提出“早期爆发警报”以在实验室结果之前检测爆发。所开发算法的一个关键点是对估计中不确定性的详细跟踪，因为警报使用可观察对象的重要性作为检测异常的主要指标。我们在Villa Azul（Quilmes）中展示了显式示例的详细信息，其中该工具对于控制按时爆发至关重要。所开发的工具是根据开发时的可用数据而紧急设计的，因此有其局限性，我们将对此进行描述和讨论。我们考虑对工具进行可能的改进，其中许多工具目前正在开发中。