Intrinsic generation time of the SARS-CoV-2 Omicron variant: An observational study of household transmission.

Bayesian inference COVID-19 Contact tracing Generation time Omicron SARS-CoV-2

Journal

The Lancet regional health. Europe
ISSN: 2666-7762
Titre abrégé: Lancet Reg Health Eur
Pays: England
ID NLM: 101777707

Informations de publication

Date de publication:
Aug 2022
Historique:
entrez: 6 7 2022
pubmed: 7 7 2022
medline: 7 7 2022
Statut: ppublish

Résumé

Starting from the final months of 2021, the SARS-CoV-2 Omicron variant expanded globally, swiftly replacing Delta, the variant that was dominant at the time. Many uncertainties remain about the epidemiology of Omicron; here, we aim to estimate its generation time. We used a Bayesian approach to analyze 23,122 SARS-CoV-2 infected individuals clustered in 8903 households as determined from contact tracing operations in Reggio Emilia, Italy, throughout January 2022. We estimated the distribution of the intrinsic generation time (the time between the infection dates of an infector and its secondary cases in a fully susceptible population), realized household generation time, realized serial interval (time between symptom onset of an infector and its secondary cases), and contribution of pre-symptomatic transmission. We estimated a mean intrinsic generation time of 6.84 days (95% credible intervals, CrI, 5.72-8.60), and a mean realized household generation time of 3.59 days (95%CrI: 3.55-3.60). The household serial interval was 2.38 days (95%CrI 2.30-2.47) with about 51% (95%CrI 45-56%) of infections caused by symptomatic individuals being generated before symptom onset. These results indicate that the intrinsic generation time of the SARS-CoV-2 Omicron variant might not have shortened as compared to previous estimates on ancestral lineages, Alpha and Delta, in the same geographic setting. Like for previous lineages, pre-symptomatic transmission appears to play a key role for Omicron transmission. Estimates in this study may be useful to design quarantine, isolation and contact tracing protocols and to support surveillance (e.g., for the accurate computation of reproduction numbers). The study was partially funded by EU grant 874850 MOOD.

Sections du résumé

Background UNASSIGNED
Starting from the final months of 2021, the SARS-CoV-2 Omicron variant expanded globally, swiftly replacing Delta, the variant that was dominant at the time. Many uncertainties remain about the epidemiology of Omicron; here, we aim to estimate its generation time.
Methods UNASSIGNED
We used a Bayesian approach to analyze 23,122 SARS-CoV-2 infected individuals clustered in 8903 households as determined from contact tracing operations in Reggio Emilia, Italy, throughout January 2022. We estimated the distribution of the intrinsic generation time (the time between the infection dates of an infector and its secondary cases in a fully susceptible population), realized household generation time, realized serial interval (time between symptom onset of an infector and its secondary cases), and contribution of pre-symptomatic transmission.
Findings UNASSIGNED
We estimated a mean intrinsic generation time of 6.84 days (95% credible intervals, CrI, 5.72-8.60), and a mean realized household generation time of 3.59 days (95%CrI: 3.55-3.60). The household serial interval was 2.38 days (95%CrI 2.30-2.47) with about 51% (95%CrI 45-56%) of infections caused by symptomatic individuals being generated before symptom onset.
Interpretation UNASSIGNED
These results indicate that the intrinsic generation time of the SARS-CoV-2 Omicron variant might not have shortened as compared to previous estimates on ancestral lineages, Alpha and Delta, in the same geographic setting. Like for previous lineages, pre-symptomatic transmission appears to play a key role for Omicron transmission. Estimates in this study may be useful to design quarantine, isolation and contact tracing protocols and to support surveillance (e.g., for the accurate computation of reproduction numbers).
Funding UNASSIGNED
The study was partially funded by EU grant 874850 MOOD.

Identifiants

pubmed: 35791373
doi: 10.1016/j.lanepe.2022.100446
pii: S2666-7762(22)00140-5
pmc: PMC9246701
doi:

Types de publication

Journal Article

Langues

eng

Pagination

100446

Informations de copyright

© 2022 The Author(s).

Déclaration de conflit d'intérêts

MA has received research funding from Seqirus. The funding is not related to COVID-19. All other authors declare no conflicts of interest.

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Auteurs

Mattia Manica (M)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.

Alfredo De Bellis (A)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Department of Mathematics, University of Trento, Trento, Italy.

Giorgio Guzzetta (G)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.

Pamela Mancuso (P)

Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy.

Massimo Vicentini (M)

Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy.

Francesco Venturelli (F)

Public Health Department, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy.

Alessandro Zerbini (A)

Unit of Clinical Immunology, Allergy and Advanced Biotechnologies, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Italy.

Eufemia Bisaccia (E)

Public Health Department, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy.

Maria Litvinova (M)

Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.

Francesco Menegale (F)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Department of Mathematics, University of Trento, Trento, Italy.

Carla Molina Grané (C)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Department of Mathematics, University of Trento, Trento, Italy.

Piero Poletti (P)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.

Valentina Marziano (V)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.

Agnese Zardini (A)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.

Valeria d'Andrea (V)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.

Filippo Trentini (F)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.
Dondena Centre for Research on Social Dynamics and Public Policy, Bocconi University, Milan, Italy.

Antonino Bella (A)

Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Rome, Italy.

Flavia Riccardo (F)

Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Rome, Italy.

Patrizio Pezzotti (P)

Dipartimento di Malattie Infettive, Istituto Superiore di Sanità, Rome, Italy.

Marco Ajelli (M)

Laboratory for Computational Epidemiology and Public Health, Department of Epidemiology and Biostatistics, Indiana University School of Public Health, Bloomington, IN, USA.

Paolo Giorgi Rossi (P)

Epidemiology Unit, Azienda Unità Sanitaria Locale - IRCCS di Reggio Emilia, Reggio Emilia, Italy.

Stefano Merler (S)

Center for Health Emergencies, Fondazione Bruno Kessler, Trento, Italy.

Classifications MeSH