Sampling biases can also affect the estimation of transmission parameters 10, 11. Thus when mean SI equals mean GT, using SI distribution as a proxy of GT distribution may underestimate the reproduction number 10, 11, 12. In addition, the SI always has a larger variance than GT due to their different biological and clinical characteristics 9. Importantly, the SI can be negative when the infectee has onset earlier than infector as shown in the pre-symptomatic transmission for COVID-19 7, 8, while GT must be positive since the infectee’s infection time must be later than infector’s infection time. However, this parametric approximation does not always hold, as GT and SI have different distributional properties. Therefore, the entire serial interval distribution is often used to estimate the reproduction number 5, 6. Under the assumption that the infector and infectee have the same incubation period (IP) distribution, the mean SI would equal the mean GT 3, 4. ![]() Thus, in practice, the time between the illness onsets of infector and infectee, which is called the serial interval (SI), is commonly used as a proxy for the GT. It is easier to record symptom onset times. The generation time distribution shapes the relationship between epidemic growth rate and reproduction number 2, while the reproduction number has been widely used to indicate the measure of transmissibility, and is defined as the average number of secondary cases infected by one typical infector in the population.Įxact infection times are hard to observe, hence the generation time distribution is usually unobserved. ![]() The generation time (GT) distribution is one of the key transmission parameters and defined as the time between successive infections in a transmission chain. The coronavirus disease 2019 (COVID-19) pandemic has caused over 557 million cases and 6 million deaths by J1. Our proposed method provides more reliable estimation of the temporal variation in the generation time distribution, improving assessment of transmission dynamics. We identified substantial reductions over time in the serial interval and generation time distributions. 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. This approximation holds under the assumption that infectors and infectees share the same incubation period distribution, which may not always be true. However, because exact infection times are rarely known, it is often approximated by the serial interval distribution. ![]() The generation time distribution, reflecting the time between successive infections in transmission chains, is a key epidemiological parameter for describing COVID-19 transmission dynamics.
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