A phenomenological estimate of the Covid-19 true scale from primary data

TL;DR

This paper presents a straightforward phenomenological method to estimate the true scale of Covid-19 infections using official epidemiological data, addressing the gap of undocumented cases crucial for policy decisions.

Contribution

It introduces a simple yet effective approach to estimate undocumented Covid-19 cases from primary data, enhancing understanding of the pandemic's true extent.

Findings

Estimated the actual number of Covid-19 infections in major EU countries and USA.

Provided insights into the scale of uncounted Covid-19 cases.

Supported public health policy formulation with more accurate infection estimates.

Abstract

Estimation of prevalence of undocumented SARS-CoV-2 infections is critical for understanding the overall impact of the Covid-19 disease. In fact, unveiling uncounted cases has fundamental implications for public policy interventions strategies. In the present work, we show a basic yet effective approach to estimate the actual number of people infected by Sars-Cov-2, by using epidemiological raw data reported by official health institutions in the largest EU countries and USA.