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

Shi Zhao, Mingwang Shen, Salihu S. Musa, Zihao Guo, Jinjun Ran, Zhihang Peng, Yu Zhao, Marc K.C. Chong, Daihai He, Maggie H. Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

21 Citations (Scopus)


Background: In infectious disease transmission dynamics, the high heterogeneity in individual infectiousness indicates that few index cases generate large numbers of secondary cases, which is commonly known as superspreading events. The heterogeneity in transmission can be measured by describing the distribution of the number of secondary cases as a negative binomial (NB) distribution with dispersion parameter, k. However, such inference framework usually neglects the under-ascertainment of sporadic cases, which are those without known epidemiological link and considered as independent clusters of size one, and this may potentially bias the estimates. Methods: In this study, we adopt a zero-truncated likelihood-based framework to estimate k. We evaluate the estimation performance by using stochastic simulations, and compare it with the baseline non-truncated version. We exemplify the analytical framework with three contact tracing datasets of COVID-19. Results: We demonstrate that the estimation bias exists when the under-ascertainment of index cases with 0 secondary case occurs, and the zero-truncated inference overcomes this problem and yields a less biased estimator of k. We find that the k of COVID-19 is inferred at 0.32 (95%CI: 0.15, 0.64), which appears slightly smaller than many previous estimates. We provide the simulation codes applying the inference framework in this study. Conclusions: The zero-truncated framework is recommended for less biased transmission heterogeneity estimates. These findings highlight the importance of individual-specific case management strategies to mitigate COVID-19 pandemic by lowering the transmission risks of potential super-spreaders with priority.

Original languageEnglish
Article number30
Pages (from-to)1-8
Number of pages8
JournalBMC Medical Research Methodology
Issue number1
Publication statusPublished - 10 Feb 2021


  • Contact tracing
  • COVID-19
  • Heterogeneity in infectiousness
  • Statistical inference
  • Superspreading
  • Transmission

ASJC Scopus subject areas

  • Epidemiology
  • Health Informatics


Dive into the research topics of 'Inferencing superspreading potential using zero-truncated negative binomial model: exemplification with COVID-19'. Together they form a unique fingerprint.

Cite this