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

Author:

Park Sang Woo1ORCID,Sun Kaiyuan2ORCID,Abbott Sam34,Sender Ron5ORCID,Bar-on Yinon M.5ORCID,Weitz Joshua S.678ORCID,Funk Sebastian34ORCID,Grenfell Bryan T.19,Backer Jantien A.10,Wallinga Jacco1011,Viboud Cecile2,Dushoff Jonathan121314ORCID

Affiliation:

1. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544

2. Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD 20892

3. Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK

4. Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK

5. Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel

6. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332

7. School of Physics, Georgia Institute of Technology, Atlanta, GA 30332

8. Institut de Biologie, École Normale Supérieure, Paris 75005, France

9. Princeton School of Public and International Affairs, Princeton University, Princeton, NJ 08542

10. Center for Infectious Disease Control, National Institute for Public Health and the Environment, 3720 Bilthoven, The Netherlands

11. Department of Biomedical Data Sciences, Leiden University Medical Center, 2333 Leiden, The Netherlands

12. Department of Biology, McMaster University, Hamilton, L8S 4L8 ON, Canada

13. Department of Mathematics and Statistics, McMaster University, Hamilton, L8S 4L8 ON, Canada

14. M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, L8S 4L8 ON, Canada

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.

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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