Estimating the generation time for SARS-CoV-2 transmission using United States household data, December 2021–May 2023
Abstract
Abstract Generation time, representing the interval between infection events in primary and secondary cases, is important for understanding disease transmission dynamics including predicting the effective reproduction number (Rt), which informs public health decisions. While previous estimates of SARS-CoV-2 generation times have been reported for early Omicron variants, there is a lack of data for subsequent sub-variants, such as XBB. We estimated SARS-CoV-2 generation times using data from the Respiratory Virus Transmission Network – Sentinel (RVTN-S) household transmission study conducted across seven U.S. sites from December 2021 to May 2023. The study spanned three Omicron sub-periods dominated by the sub-variants BA.1/2, BA.4/5, and XBB. We employed a Susceptible-Exposed-Infectious-Recovered (SEIR) model with a Bayesian data augmentation method that imputes unobserved infection times of cases to estimate the generation time. The estimated mean generation time for the overall Omicron period was 3.5 days (95% credible interval, CrI: 3.3–3.7). During the sub-periods, the estimated mean generation times were 3.8 days (95% CrI: 3.4–4.2) for BA.1/2, 3.5 days (95% CrI: 3.3–3.8) for BA.4/5, and 3.5 days (95% CrI: 3.1–3.9) for XBB. Our study provides estimates of generation times for the Omicron variant, including the sub-variants BA.1/2, BA.4/5, and XBB. These up-to-date estimates specifically address the gap in knowledge regarding these sub-variants and are consistent with earlier studies. They enhance our understanding of SARS-CoV-2 transmission dynamics by aiding in the prediction of Rt, offering insights for improving COVID-19 modeling and public health strategies.
Article Details
Authors (108)
Louis Yat Hin Chan
Sinead E. Morris
Melissa S. Stockwell
Natalie M. Bowman
Edwin Asturias
Suchitra Rao
Karen Lutrick
Katherine D. Ellingson
Huong Q. Nguyen
Yvonne Maldonado
Son H. McLaren
Ellen Sano
Jessica E. Biddle
Sarah E. Smith-Jeffcoat
Matthew Biggerstaff
Influenza Division, Centers for Disease Control and Prevention
Melissa A. Rolfes
Carlos G. Grijalva
Rebecca K. Borchering
Influenza Division, Centers for Disease Control and Prevention
Alexandra M. Mellis
Influenza Division, Centers for Disease Control and Prevention, Atlanta
H. Keipp Talbot
Vanderbilt University Medical Center, Nashville
Lisa Saiman
Raul A. Silverio Francisco
Anny L.Diaz Perez
Ana M. Valdez de Romero
Ayla Bullock
Amy Yang
Quenla Haehnel
Jessica Lin
Julienne Reynolds
Katherine Katie Murray
Miriana Moreno Zivanovich
Anna McShea
Brittney Figueroa
Melody Liu
Kathleen Grice
Cameron Bendalin
Sonia Chavez
Jolie Granger
Ferris Alaa Ramadan
Flavia Maria Nakayima Miiro
Josue Ortiz
Mokenge Ndiva Mongoh
Edward A. Belongia
Hannah Berger
Vicki Moon
Gina Burbey
Leila Deering
Brianna Freund
Garrett Heuer
Sarah Kopitzke
Carrie Marcis
Jennifer Meece
Jennifer Moran
DeeAnn Hertel
Joshua Petrie
Miriah Rotar
Carla Rottscheit
Elisha Stefanski
Sandy Strey
Melissa Strupp
Rosita Thiessen
Marcela Lopez
Alondra A. Aguilar
Emma Stainton
Grace K-Y. Tam
Jonathan Altamirano
Leanne X. Chun
Rasika Behl
Samantha A. Ferguson
Yuan J. Carrington
Frank S. Zhou
Chris Lindsell
Judy King
John Meghreblian
Samuel Massion
Brittany Creasman
Lauren Milner
Andrea Stafford Hintz
Jorge Celedonio
Ryan Dalforno
Maria Catalina Padilla-Azain
Daniel Chandler
Paige Yates
Brianna Schibley-Laird
Alexis Perry
Ruby Swaimn
Mason Speirs
Erica Anderson
Suryakala Sarilla
Amelia Dodds
Dayton Marchlewski
Timothy Williams
Afan Swan
Onika Abrams
Jackson Resser
Ine Sohn
Cara Lwin
Hsi-nien Jubilee Tan
Stephen Yeargin
James Grindstaff
Heather Prigmore
Jessica Lai
Zhouwen Liu
James D. Chappell
Marcia Blair
Rendie E. McHenry
Bryan P. M. Peterson
Lauren J. Ezzell