COVID-19 mild cases determination from correlating COVID-line calls to reported cases

TL;DR

This paper introduces a simple algorithm that correlates COVID-line calls with reported cases to estimate the number of mild, untested COVID-19 cases, providing an early and inexpensive measure of the pandemic's true scale.

Contribution

The novel method models COVID-line calls as a sum of signal and background to estimate unreported mild cases, enhancing understanding of COVID-19 spread.

Findings

In Buenos Aires, 6.6 calls per reported case were observed.

Estimated 20 symptomatic cases per reported case in Buenos Aires.

The method offers early, cost-effective estimates of unreported COVID-19 cases.

Abstract

Background: One of the most challenging keys to understand COVID-19 evolution is to have a measure on those mild cases which are never tested because their few symptoms are soft and/or fade away soon. The problem is not only that they are difficult to identify and test, but also that it is believed that they may constitute the bulk of the cases and could be crucial in the pandemic equation. Methods: We present a novel algorithm to extract the number of these mild cases by correlating a COVID-line calls to reported cases in given districts. The key assumption is to realize that, being a highly contagious disease, the number of calls by mild cases should be proportional to the number of reported cases. Whereas a background of calls not related to infected people should be proportional to the district population. Results: We find that for Buenos Aires Province, in addition to the background, there are in signal 6.6 +/- 0.4 calls per each reported COVID-19 case. Using this we estimate in Buenos Aires Province 20 +/- 2 COVID-19 symptomatic cases for each one reported. Conclusions: A very simple algorithm that models the COVID-line calls as sum of signal plus background allows to estimate the crucial number of the rate of symptomatic to reported COVID-19 cases in a given district. The result from this method is an early and inexpensive estimate and should be contrasted to other methods such as serology and/or massive testing.