TY - JOUR
T1 - Inferencing superspreading potential using zero-truncated negative binomial model
T2 - exemplification with COVID-19
AU - Zhao, Shi
AU - Shen, Mingwang
AU - Musa, Salihu S.
AU - Guo, Zihao
AU - Ran, Jinjun
AU - Peng, Zhihang
AU - Zhao, Yu
AU - Chong, Marc K.C.
AU - He, Daihai
AU - Wang, Maggie H.
N1 - Funding Information:
DH was supported by General Research Fund (Grant Number 15205119) of the Research Grants Council (RGC) of Hong Kong, China, and an Alibaba (China) Co. Ltd. Collaborative Research grant.
Publisher Copyright:
© 2021, The Author(s).
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
KW - Contact tracing
KW - COVID-19
KW - Heterogeneity in infectiousness
KW - Statistical inference
KW - Superspreading
KW - Transmission
UR - http://www.scopus.com/inward/record.url?scp=85101031067&partnerID=8YFLogxK
U2 - 10.1186/s12874-021-01225-w
DO - 10.1186/s12874-021-01225-w
M3 - Journal article
C2 - 33568100
AN - SCOPUS:85101031067
VL - 21
SP - 1
EP - 8
JO - BMC Medical Research Methodology
JF - BMC Medical Research Methodology
SN - 1471-2288
IS - 1
M1 - 30
ER -