A quantitative assessment of epidemiological parameters required to investigate COVID-19 burden.

Contact tracing data Disease burden Epidemiological parameters Risk outcomes SARS-CoV-2

Journal

Epidemics
ISSN: 1878-0067
Titre abrégé: Epidemics
Pays: Netherlands
ID NLM: 101484711

Informations de publication

Date de publication:
12 2021
Historique:
received: 10 05 2021
revised: 07 10 2021
accepted: 12 11 2021
pubmed: 27 11 2021
medline: 21 12 2021
entrez: 26 11 2021
Statut: ppublish

Résumé

Solid estimates describing the clinical course of SARS-CoV-2 infections are still lacking due to under-ascertainment of asymptomatic and mild-disease cases. In this work, we quantify age-specific probabilities of transitions between stages defining the natural history of SARS-CoV-2 infection from 1965 SARS-CoV-2 positive individuals identified in Italy between March and April 2020 among contacts of confirmed cases. Infected contacts of cases were confirmed via RT-PCR tests as part of contact tracing activities or retrospectively via IgG serological tests and followed-up for symptoms and clinical outcomes. In addition, we provide estimates of time intervals between key events defining the clinical progression of cases as obtained from a larger sample, consisting of 95,371 infections ascertained between February and July 2020. We found that being older than 60 years of age was associated with a 39.9% (95%CI: 36.2-43.6%) likelihood of developing respiratory symptoms or fever ≥ 37.5 °C after SARS-CoV-2 infection; the 22.3% (95%CI: 19.3-25.6%) of the infections in this age group required hospital care and the 1% (95%CI: 0.4-2.1%) were admitted to an intensive care unit (ICU). The corresponding proportions in individuals younger than 60 years were estimated at 27.9% (95%CI: 25.4-30.4%), 8.8% (95%CI: 7.3-10.5%) and 0.4% (95%CI: 0.1-0.9%), respectively. The infection fatality ratio (IFR) ranged from 0.2% (95%CI: 0.0-0.6%) in individuals younger than 60 years to 12.3% (95%CI: 6.9-19.7%) for those aged 80 years or more; the case fatality ratio (CFR) in these two age classes was 0.6% (95%CI: 0.1-2%) and 19.2% (95%CI: 10.9-30.1%), respectively. The median length of stay in hospital was 10 (IQR: 3-21) days; the length of stay in ICU was 11 (IQR: 6-19) days. The obtained estimates provide insights into the epidemiology of COVID-19 and could be instrumental to refine mathematical modeling work supporting public health decisions.

Identifiants

pubmed: 34826786
pii: S1755-4365(21)00074-8
doi: 10.1016/j.epidem.2021.100530
pmc: PMC8595250
pii:
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

100530

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

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Auteurs

Agnese Zardini (A)

Bruno Kessler Foundation, Trento, Italy.

Margherita Galli (M)

Bruno Kessler Foundation, Trento, Italy; Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy.

Marcello Tirani (M)

Directorate General for Health, Lombardy Region, Milan, Italy; Health Protection Agency of the Metropolitan Area of Milan, Milano, Italy.

Danilo Cereda (D)

Directorate General for Health, Lombardy Region, Milan, Italy.

Mattia Manica (M)

Bruno Kessler Foundation, Trento, Italy.

Filippo Trentini (F)

Bruno Kessler Foundation, Trento, Italy; Dondena Centre for Research on Social Dynamics and Public Policy, and CovidCrisisLab, Bocconi University, Milan, Italy.

Giorgio Guzzetta (G)

Bruno Kessler Foundation, Trento, Italy.

Valentina Marziano (V)

Bruno Kessler Foundation, Trento, Italy.

Raffaella Piccarreta (R)

Dondena Centre for Research on Social Dynamics and Public Policy, and CovidCrisisLab, Bocconi University, Milan, Italy; Department of Decision Sciences, Bocconi University, Milan, Italy.

Alessia Melegaro (A)

Dondena Centre for Research on Social Dynamics and Public Policy, and CovidCrisisLab, Bocconi University, Milan, Italy; Department of Social and Political Sciences, Bocconi University, Milan, Italy.

Marco Ajelli (M)

Laboratory for Computational Epidemiology and Public Health, Indiana University School of Public Health, Bloomington, United States.

Piero Poletti (P)

Bruno Kessler Foundation, Trento, Italy. Electronic address: poletti@fbk.eu.

Stefano Merler (S)

Bruno Kessler Foundation, Trento, Italy.

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