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Table 1 The summary of the dispersion parameter, k, estimates of COVID-19 transmission in the existing literature and this study. The highlighted estimates are considered as main results in this study

From: Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19

type of dataset data source truncation dispersion parameter, k estimated in sporadic case included
offspring # of each case Dataset #1: Xu et al. [8] (n = 2214) No 0.70 (0.59, 0.98) He et al. [12] No
0.72 (0.63, 0.89) this study
Yes 0.37 (0.29, 0.48)
Dataset #2:
Adam et al. [11] (n = 290)
No 0.43 (0.29, 0.67) Adam et al. [11] Yes
0.42 (0.26, 0.78) this study
Yes 0.32 (0.15, 0.64) No
Dataset #3:
Zhang et al. [13] (n = 47)
No 0.25 (0.13, 0.88) Zhang et al. [13] Yes
0.22 (0.03, 1.15) this study
Yes 0.18 (0.01, 1.79) No
not included in this study No 0.58 (0.35, 1.18) Bi et al. [34] Yes
range: 0.32–0.82 Lau et al. [22]
0.11 (0.05, 0.25) Tariq et al. [35]
outbreak size not applicable 0.54 (0.01, 6.95) Riou et al. [3] irrelevant
0.10 (0.05, 0.20) Endo et al. [36]
genome sequences 0.32 (0.13, 0.38) Wang et al. [37]
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