Estimating COVID-19 mortality in Italy early in the COVID-19 pandemic

Estimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate morta...

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Bibliographic Details
Published in:Nature communications 2021-05, Vol.12 (1), p.2729-2729, Article 2729
Main Authors: Modi, Chirag, Böhm, Vanessa, Ferraro, Simone, Stein, George, Seljak, Uroš
Format: Article
Language:English
Subjects:
631/114/2415
631/326/596/4130
692/699/255/2514
692/700/478/174
Confidence intervals
Coronaviruses
COVID-19
Cruise ships
Diamonds
Estimation
Fatalities
Humanities and Social Sciences
Infections
Mortality
multidisciplinary
Pandemics
Science
Science (multidisciplinary)
Ships
Statistical analysis
Time series
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Summary:Estimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo. Estimates of COVID-19-related mortality are limited by incomplete testing. Here, the authors perform counterfactual analyses and estimate that there were 59,000–62,000 deaths from COVID-19 in Italy until 9 th September 2020, approximately 1.5 times higher than official statistics.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-021-22944-0