Correction of Italian under-reporting in the first COVID-19 wave via age-specific deconvolution of hospital admissions
Simone Milanesi, Giuseppe De Nicolao

TL;DR
This paper develops an age-specific deconvolution method to correct for under-reporting of COVID-19 cases in Italy's first wave, providing more accurate estimates aligned with serological data.
Contribution
It introduces a regularization-based inverse problem approach for correcting under-reported infection data using hospital admissions, specifically tailored to age groups.
Findings
Estimated total infections are about double the official counts.
Corrected infection time series align with serological survey data.
Method improves retrospective epidemiological assessments.
Abstract
When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By…
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Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 Clinical Research Studies · COVID-19 diagnosis using AI
