Evidence map›Paper›PMID 37216529›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2023

Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2.

Sang Woo Park, Kaiyuan Sun, Sam Abbott, Ron Sender, Yinon M Bar-On, Joshua S Weitz, Sebastian Funk, Bryan T Grenfell, Jantien A Backer, Jacco Wallinga and 2 more

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Sang Woo ParkDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.ORCID 0000-0003-2202-3361
Kaiyuan SunDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892.ORCID 0000-0002-1753-9884
Sam AbbottCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.
Ron SenderDepartment of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.ORCID 0000-0002-1165-9818
Yinon M Bar-OnDepartment of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.ORCID 0000-0001-8477-609X
Joshua S WeitzSchool of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332.ORCID 0000-0002-3433-8312
Sebastian FunkCentre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK.ORCID 0000-0002-2842-3406
Bryan T GrenfellDepartment of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544.
Jantien A BackerCenter for Infectious Disease Control, National Institute for Public Health and the Environment, 3720 Bilthoven, The Netherlands.
Jacco WallingaCenter for Infectious Disease Control, National Institute for Public Health and the Environment, 3720 Bilthoven, The Netherlands.
Cecile ViboudDivision of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892.
Jonathan DushoffDepartment of Biology, McMaster University, Hamilton, L8S 4L8 ON, Canada.ORCID 0000-0003-0506-4794

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Estimating the differences in the incubation-period, serial-interval, and generation-interval distributions of SARS-CoV-2 variants is critical to understanding their transmission. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection-for example, when an epidemic is growing exponentially, a cohort of infected individuals who developed symptoms at the same time are more likely to have been infected recently. Here, we reanalyze incubation-period and serial-interval data describing transmissions of the Delta and Omicron variants from the Netherlands at the end of December 2021. Previous analysis of the same dataset reported shorter mean observed incubation period (3.2 d vs. 4.4 d) and serial interval (3.5 d vs. 4.1 d) for the Omicron variant, but the number of infections caused by the Delta variant decreased during this period as the number of Omicron infections increased. When we account for growth-rate differences of two variants during the study period, we estimate similar mean incubation periods (3.8 to 4.5 d) for both variants but a shorter mean generation interval for the Omicron variant (3.0 d; 95% CI: 2.7 to 3.2 d) than for the Delta variant (3.8 d; 95% CI: 3.7 to 4.0 d). The differences in estimated generation intervals may be driven by the "network effect"-higher effective transmissibility of the Omicron variant can cause faster susceptible depletion among contact networks, which in turn prevents late transmission (therefore shortening realized generation intervals). Using up-to-date generation-interval distributions is critical to accurately estimating the reproduction advantage of the Omicron variant.

Indexed as

COVID-19EpidemicsHumansNetherlandsSARS-CoV-2epidemiologygeneration intervalOmicron variantSARS-CoV-2

Identifiers

PMID37216529
PMCPMC10235974

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.