Inferring time-varying generation time, serial interval, and incubation period distributions for COVID-19

• Published in: Nature communications Vol. 13; no. 1; pp. 7727 - 12
• Main Authors: Chen, Dongxuan, Lau, Yiu-Chung, Xu, Xiao-Ke, Wang, Lin, Du, Zhanwei, Tsang, Tim K., Wu, Peng, Lau, Eric H. Y., Wallinga, Jacco, Cowling, Benjamin J., Ali, Sheikh Taslim
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2415; 631/326/596/4130; 692/699/255; 692/700/478/174; Approximation; Chains; China - epidemiology; Coronaviruses; COVID-19; COVID-19 - epidemiology; Disease transmission; Epidemic models; Epidemiology; Estimation; Humanities and Social Sciences; Humans; Infections; Infectious Disease Incubation Period; multidisciplinary; Pandemics; Parameters; Public health; SARS-CoV-2; Science; Science (multidisciplinary); Temporal variations; Time Factors; China
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-35496-8

The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. We estimated incubation period and serial interval distributions using 629 transmission pairs reconstructed by investigating 2989 confirmed cases in China in January-February 2020, and developed an inferential framework to estimate the generation time distribution that accounts for variation over time due to changes in epidemiology, sampling biases and public health and social measures. We identified substantial reductions over time in the serial interval and generation time distributions. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics. The generation time (interval between successive infections in a transmission chain) is an important parameter for epidemiological modeling. Here, the authors develop a framework for estimating this parameter and how it changes over time and apply it to data from China in the first months of the pandemic.