Inferring the differences in incubation-period and generation-interval distributions of the Delta and Omicron variants of SARS-CoV-2
Abstract
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 and control. However, the impact of epidemic dynamics is often neglected in estimating the timing of infection and transmission—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 re-analyze 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 data set reported shorter mean observed incubation period (3.2 days vs 4.4 days) and serial interval (3.5 days vs 4.1 days) 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 (3.8–4.5 days) for both variants but a shorter mean generation interval for the Omicron variant (3.0 days; 95% CI: 2.7–3.2 days) than for the Delta variant (3.8 days; 95% CI: 3.7–4.0 days). We further note that 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. Significance Recent studies suggest that individuals infected with the Omicron variant develop symptoms earlier (shorter incubation period) and transmit faster (shorter generation interval) than those infected with the Delta variant. However, these studies typically neglect population-level effects: when an epidemic is growing, a greater proportion of current cases were infected recently, biasing us toward observing faster transmission events. Accounting for this dynamical bias, we find that Omicron infections from the Netherlands at the end of December 2021 had similar incubation periods, but shorter generation intervals, compared to Delta infections from the same period. Shorter generation intervals of the Omicron variant might be due to its higher effective reproduction number, which can cause faster local susceptible depletion around the contact network.
Authors 12
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Sang Woo Park corresponding
Affiliation as printed
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
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National Institutes of Health · Fogarty International Center
Affiliation as printed
Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
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Affiliation as printed
Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
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Affiliation as printed
Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot, Israel
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Georgia Institute of Technology · École Normale Supérieure - PSL · Institut de Biologie de l'École Normale Supérieure
Affiliation as printed
Institut de Biologie, École Normale Supérieure, Paris, France
School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA
School of Physics, Georgia Institute of Technology, Atlanta, GA, USA
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London School of Hygiene & Tropical Medicine
Affiliation as printed
Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK
Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
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Affiliation as printed
Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA
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National Institute for Public Health and the Environment
Affiliation as printed
Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
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Leiden University Medical Center · National Institute for Public Health and the Environment
Affiliation as printed
Center for Infectious Disease Control, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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National Institutes of Health · Fogarty International Center
Affiliation as printed
Division of International Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
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Affiliation as printed
Department of Biology, McMaster University, Hamilton, ON, Canada
Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada
M. G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada
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