An empirical estimate of the infection fatality rate of COVID-19 from the first Italian outbreak

  1. Gianluca Rinaldi
  2. Matteo Paradisi

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

The coronavirus 2019 (COVID-19) pandemic has been spreading globally for months, yet the infection fatality ratio of the disease is still uncertain. This is partly because of inconsistencies in testing and death reporting standards across countries. We provide estimates which don’t rely on official cases and deaths data but only on population level statistics.

Methods

We collected demographic and death records data from the Italian Institute of Statistics. We focus on the area in Italy that experienced the initial outbreak of COVID-19 and estimated a Bayesian model fitting age-stratified mortality data from 2020 and previous years. We also assessed the sensitivity of our results to alternative assumptions on the proportion of population infected. Based on our estimates we finally studied the heterogeneity in overall lethality across countries.

Findings

We estimate an overall infection fatality rate of 1.31% (95% credible interval [CrI] 0.94 – 1.89), as well as large differences by age, with a low infection fatality rate of 0.05% for under 60 year old (CrI 0 – 0.17) and a substantially higher 4.16% (CrI 3.05 – 5.80) for people above 60 years of age. In our sensitivity analysis, we found that even under extreme assumptions, our method delivered useful information. For instance, even if only 10% of the population were infected, the infection fatality rate would not rise above 0.2% for people under 60. Finally, using data on demographics we show large expected heterogeneity in overall IFR across countries.

Interpretation

Our empirical estimates show a sharp difference in fatality rates between young and old people and rule out overall fatality ratios below 0.5% in populations with more than 30% over 60 years old.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.18.20070912: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    One limitation of our estimates is that they should not be taken at face value in the analysis of contexts where the hospital system is under stress or close to capacity. This is because our exercise was performed on a case study at the beginning of the Italian outbreak when the hospital system was still fully functioning. Moreover, the quarantine measures implemented in the nine municipalities on February 21st likely reduced contagion, potentially affecting infection fatality ratios and making them hard to extrapolate to context where similar measures were not undertaken. Another limitation is that our model assumed a constant baseline lethality rate in absence of COVID-19. This implies that the COVID-19 outbreak did not change the baseline death rate in the population. Plausibly, the lockdown policy decreased deaths from, among others, violence and traffic, while at the same time the outbreak could have increased other fatalities due to lower availability of healthcare resources for other diseases. Fluctuations due to these causes are, however, likely to be quantitatively small compared to the large spike in deaths that we observed in 2020, where total deaths were five times the average in previous years over the same period of time (371 vs 73). For this reason, confounding factors in baseline deaths levels should not have substantially affected the signal to noise ratio of our death data. In conclusion, our results support the need for isolating policies especially for the...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.18.20070912 on medRxiv