Since the early outbreak of coronavirus disease 2019 (COVID-19) pandemic, huge efforts have been devoted on estimating key epidemiological parameters due to their important implication in mitigation planning. For instance, according to a survey posted in a public domain (
https://github.com/midas-network/COVID-19/tree/master/parameter_estimates/2019_novel_coronavirus), there were at least 47 studies (either peer-reviewed or not) on the cumulative case count in a location have been posted, 39 works on the reproductive number
R0 (number of secondary cases may be cause by a typical primary cases), 13 on the incubation period (time delay between infection and symptom onset), 6 on the serial interval or generation interval (time delay between symptom onset or infection of an index case and its secondary case in a transmission chain), 6 on the symptomatic case fatality ratio. However, the individual variation in infectiousness, the dispersion rate (
k), has been largely overlooked, except for one early work in Eurosurveillance [
1]. He et al. (2020) summarized the recent estimates on
k from empirical offspring distributions, including 0.58 (95% confidence interval [CI]: 0.35, 1.18) of Bi et al. (2020) from a sample of 391 COVID-19 cases in Shenzhen China [
2]. It is of note that there is mathematical modelling work based on imported and reported case numbers in a variety of countries showing that
k could be 0.1 (95% CI: 0.05, 0.2) [
3]. The recent study of Lau et al. [
4] used a spatiotemporal transmission process model and estimated that overall dispersion parameter
k is 0.45 for Cobb County, 0.43 for Dekalb, 0.39 for Fulton, 0.49 for Gwinnett, and 0.32 for Dougherty in Georgia, USA. In this work, with a larger dataset, we calculate
k using the empirical offspring distribution approach. Our data are from mainland China where strict surveillance guaranteed the quality of the data. Since we adopted the basic definition approach, our methods do not rely on additional assumptions typically needed for mathematical modelling.