Inference of the SARS-CoV-2 generation time using UK household data

Area of Science:

• Epidemiology
• Infectious Disease Dynamics
• Public Health

Background:

• The generation time of SARS-CoV-2, the interval between infection and transmission, is key to understanding transmission dynamics and informing public health interventions.
• Previous estimates of the generation time distribution were primarily based on early pandemic data.
• Updated estimates are needed to reflect potential changes in transmission characteristics over time.

Purpose of the Study:

• To provide updated estimates of the SARS-CoV-2 generation time distribution using data from a UK household study conducted from March to November 2020.
• To compare estimates from a standard model (assuming independent transmission and symptoms) with a mechanistic model (linking transmission and symptoms).
• To investigate whether the generation time has varied temporally during the study period.

Main Methods:

• Utilized data from a UK household transmission study (March-November 2020).
• Employed two modeling approaches: a standard model assuming independence between transmission and symptom onset, and a mechanistic model explicitly linking them.
• Analyzed data to estimate mean generation time, standard deviation, and temporal variations.

Main Results:

• The standard model estimated a mean generation time of 4.2 days (95% CI: 3.3-5.3) with a higher standard deviation (4.9 days, 95% CI: 3.0-8.3) compared to previous estimates.
• The mechanistic model estimated a longer mean generation time of 5.9 days (95% CI: 5.2-7.0) with a similar standard deviation (4.8 days, 95% CI: 4.0-6.3).
• Both models indicated a shorter mean generation time in the later months of the study (September-November 2020) compared to earlier periods.

Conclusions:

• The SARS-CoV-2 generation time appears to have changed during the study period.
• Updated estimates, particularly from mechanistic models, are essential for accurate transmission assessment.
• Continued data collection and analysis are vital for ongoing monitoring and informing future public health policy decisions regarding COVID-19 transmission.