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Published on: September 22, 2010
Estimating effective reproduction number using generation time versus serial interval, with application to covid-19
Jesse Knight1,2, Sharmistha Mishra1,2,3,4
1Institute of Medical Science, University of Toronto, Canada.
Background:
The effective reproduction number R (t) is a critical measure of epidemic potential. R (t) can be calculated in near real time using an incidence time series and the generation time distribution: the time between infection events in an infector-infectee pair. In calculating R (t), the generation time distribution is often approximated by the serial interval distribution: the time between symptom onset in an infector-infectee pair. However, while generation time must be positive by definition, serial interval can be negative if transmission can occur before symptoms, such as in covid-19, rendering such an approximation improper in some contexts.
Methods:
We developed a method to infer the generation time distribution from parametric definitions of the serial interval and incubation period distributions. We then compared estimates of R (t) for covid-19 in the Greater Toronto Area of Canada using: negative-permitting versus non-negative serial interval distributions, versus the inferred generation time distribution.
Results:
We estimated the generation time of covid-19 to be Gamma-distributed with mean 3.99 and standard deviation 2.96 days. Relative to the generation time distribution, non-negative serial interval distribution caused overestimation of R (t) due to larger mean, while negative-permitting serial interval distribution caused underestimation of R (t) due to larger variance.
Implications:
Approximation of the generation time distribution of covid-19 with non-negative or negative-permitting serial interval distributions when calculating R (t) may result in over or underestimation of transmission potential, respectively.
Related Concept Videos
Generation Time
Population Growth
Exponential Equations for Modeling Growth
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Estimating Population Standard Deviation
Statistical Methods for Analyzing Epidemiological Data

