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<p>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.</p>

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Estimating the generation time for SARS-CoV-2 transmission using United States household data, December 2021–May 2023

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月11日

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  • 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,
  • Melissa A. Rolfes,
  • H. Keipp Talbot,
  • Carlos G. Grijalva,
  • Rebecca K. Borchering,
  • Alexandra M. Mellis,
  • H. Keipp Talbot,
  • 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

摘要

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.