Abstract
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.
Highlights
The COVID-19 epidemic has been causing global damage to practically all aspects of world society since early 2020
The difficulties in controlling the epidemic are in part due to a crucial combination of being highly contagious [1], having a long incubation period [2] during which infections are possible a few days before symptoms onset [3], having mild or asymptomatic cases [1] and because the diagnosis may take a few days after contacting the Health Care system
We present in this work a method to mitigate the epidemic effects by estimating the number of COVID-19 cases without having to wait for laboratory confirmations
Summary
The COVID-19 epidemic has been causing global damage to practically all aspects of world society since early 2020. The difficulties in controlling the epidemic are in part due to a crucial combination of being highly contagious [1], having a long incubation period [2] during which infections are possible a few days before symptoms onset [3], having mild or asymptomatic cases [1] and because the diagnosis may take a few days after contacting the Health Care system The latter yields that outbreaks spread and epidemic evolves while laboratory results are being processed. If the person has symptoms that could indicate a COVID19 infection, they are instructed to follow the corresponding protocol Such a syndromic database was used as an input for estimation of cases and outbreak detection in Buenos Aires Province.
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