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One of the main problems in controlling COVID-19 epidemic spread is the delayin confirming cases. Having information on changes in the epidemic evolution oroutbreaks rise before lab-confirmation is crucial in decision making for PublicHealth policies. We present an algorithm to estimate on-stream the number ofCOVID-19 cases using the data from telephone calls to a COVID-line. By modelingthe calls as background (proportional to population) plus signal (proportionalto infected), we fit the calls in Province of Buenos Aires (Argentina) withcoefficient of determination $R^2 > 0.85$. This result allows us to estimatethe number of cases given the number of calls from a specific district, daysbefore the lab results are available. We validate the algorithm with real data.We show how to use the algorithm to track on-stream the epidemic, and presentthe Early Outbreak Alarm to detect outbreaks in advance to lab results. One keypoint in the developed algorithm is a detailed track of the uncertainties inthe estimations, since the alarm uses the significance of the observables as amain indicator to detect an anomaly. We present the details of the explicitexample in Villa Azul (Quilmes) where this tool resulted crucial to control anoutbreak on time. The presented tools have been designed in urgency with theavailable data at the time of the development, and therefore have theirlimitations which we describe and discuss. We consider possible improvements onthe tools, many of which are currently under development.

Estimating COVID-19 cases and outbreaks on-stream through phone-calls