Robustly estimating the COVID19 epidemic curve in northern Italy using all-cause mortality
Luca Presotto

TL;DR
This study reconstructs the COVID-19 epidemic curve in northern Italy using all-cause mortality data and models to estimate the reproduction number, revealing the impact of containment measures and early undetected spread.
Contribution
It introduces a method to estimate the epidemic dynamics from mortality data, providing insights into the timing and effectiveness of interventions in a region with limited early case data.
Findings
Reproduction number was 2.6 before first case detection.
School closures reduced R to 1.3.
Lockdowns lowered R below 0.8, with no further reduction after hard lockdown.
Abstract
Background: Northern Italy was one of the most impacted areas by COVID. It is now widely assumed that the virus was silently spreading for at least 2 weeks before the first patient was identified. During this silent phase, and in the following weeks when the hospital system was overburdened, data collection was not performed in an accurate enough way to estimate an epidemic curve. With the aim of assessing both the dynamics of the introduction of the virus and the effectiveness of containment measures introduced, we try to reconstruct the epidemic curve using all cause mortality data. Methods: we collected all cause mortality data stratified by age from the national institute of statistics, together with COVID-related deaths data released by other government structures. Using a SEIR model together with estimates of the exposure to death time distribution, we fitted the reproduction…
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Taxonomy
TopicsCOVID-19 epidemiological studies · COVID-19 Pandemic Impacts · SARS-CoV-2 and COVID-19 Research
