Superspreading potential of infection seeded by the SARS-CoV-2 Omicron BA.1 variant in South Korea

Guo, Zihao; Zhao, Shi; Ryu, Sukhyun; Mok, Chris Ka Pun; Hung, Chi Tim; Chong, Ka Chun; Yeoh, Eng Kiong

doi:10.1016/j.jinf.2022.05.041

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…We also found that the risk of transmission and superspreading potentials varied across different contact settings. The higher estimated superspreading potential in schools and communities is consistent with a study conducted in South Korea, whereby the transmission chains in communities and schools were more heterogeneous (smaller k) than that in the household [21]. Besides Hong Kong and South Korea, compared to other contact settings, relatively more significant superspreading potential occurred in communities in some other regions since there has been a high likelihood of community gathering due to religions and folk custom, such as Kumbh Mela during April and May in India and Songkran festival in Thailand [22,23].…”

Section: Discussionsupporting

confidence: 88%

Differences in the superspreading potentials of COVID-19 across contact settings

Zhao

Guo

et al. 2022

BMC Infect Dis

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Background Superspreading events (SSEs) played a critical role in fueling the COVID-19 outbreaks. Although it is well-known that COVID-19 epidemics exhibited substantial superspreading potential, little is known about the risk of observing SSEs in different contact settings. In this study, we aimed to assess the potential of superspreading in different contact settings in Japan. Method Transmission cluster data from Japan was collected between January and July 2020. Infector-infectee transmission pairs were constructed based on the contact tracing history. We fitted the data to negative binomial models to estimate the effective reproduction number (R) and dispersion parameter (k). Other epidemiological issues relating to the superspreading potential were also calculated. Results The overall estimated R and k are 0.561 (95% CrI: 0.496, 0.640) and 0.221 (95% CrI: 0.186, 0.262), respectively. The transmission in community, healthcare facilities and school manifest relatively higher superspreading potentials, compared to other contact settings. We inferred that 13.14% (95% CrI: 11.55%, 14.87%) of the most infectious cases generated 80% of the total transmission events. The probabilities of observing superspreading events for entire population and community, household, health care facilities, school, workplace contact settings are 1.75% (95% CrI: 1.57%, 1.99%), 0.49% (95% CrI: 0.22%, 1.18%), 0.07% (95% CrI: 0.06%, 0.08%), 0.67% (95% CrI: 0.31%, 1.21%), 0.33% (95% CrI: 0.13%, 0.94%), 0.32% (95% CrI: 0.21%, 0.60%), respectively. Conclusion The different potentials of superspreading in contact settings highlighted the need to continuously monitoring the transmissibility accompanied with the dispersion parameter, to timely identify high risk settings favoring the occurrence of SSEs.

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Section: Discussionsupporting

confidence: 88%

Differences in the superspreading potentials of COVID-19 across contact settings

Zhao

Guo

et al. 2022

BMC Infect Dis

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“…Moreover, only 15% 114 to 19% 136 of individuals that were infected led to 80% of secondary transmissions of ancestral SARS-CoV-2. Similar trends were confirmed for Omicron BA.1 and BA.2, for which only 9% 137 to 20% 138 of the infectious contacts were responsible for 80% of all transmissions.…”

Section: Influence Of Viral Load On Transmissionsupporting

confidence: 73%

SARS-CoV-2 viral load and shedding kinetics

2022

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SARS-CoV-2 viral load and detection of infectious virus in the respiratory tract are the two key parameters for estimating infectiousness. As shedding of infectious virus is required for onward transmission, understanding shedding characteristics is relevant for public health interventions. Viral shedding is influenced by biological characteristics of the virus, host factors and pre-existing immunity (previous infection or vaccination) of the infected individual. Although the process of human-to-human transmission is multifactorial, viral load substantially contributed to human-to-human transmission, with higher viral load posing a greater risk for onward transmission. Emerging SARS-CoV-2 variants of concern have further complicated the picture of virus shedding. As underlying immunity in the population through previous infection, vaccination or a combination of both has rapidly increased on a global scale after almost 3 years of the pandemic, viral shedding patterns have become more distinct from those of ancestral SARS-CoV-2. Understanding the factors and mechanisms that influence infectious virus shedding and the period during which individuals infected with SARS-CoV-2 are contagious is crucial to guide public health measures and limit transmission. Furthermore, diagnostic tools to demonstrate the presence of infectious virus from routine diagnostic specimens are needed.

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“…We estimated that the dispersion number for the epidemic wave caused by the Omicron variant (in December 2021) in South Africa was smaller than that of the Delta wave (from June to August 2021) in this country, suggesting more significant heterogeneity of Omicron. This finding was consistent with the recent study on the epidemic in South Korea with much fewer infections [ 51 ], which might be a complementary explanation to the unprecedentedly fast spreading of Omicron. Our results also highlighted the need of taking more efficient measure of to reduce people gathering and the possible superspreading events [ 28 , 52 ].…”

Section: Discussionsupporting

confidence: 93%

Monitoring real-time transmission heterogeneity from incidence data

2022

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The transmission heterogeneity of an epidemic is associated with a complex mixture of host, pathogen and environmental factors. And it may indicate superspreading events to reduce the efficiency of population-level control measures and to sustain the epidemic over a larger scale and a longer duration. Methods have been proposed to identify significant transmission heterogeneity in historic epidemics based on several data sources, such as contact history, viral genomes and spatial information, which may not be available, and more importantly ignore the temporal trend of transmission heterogeneity. Here we attempted to establish a convenient method to estimate real-time heterogeneity over an epidemic. Within the branching process framework, we introduced an instant-individualheterogenous infectiousness model to jointly characterize the variation in infectiousness both between individuals and among different times. With this model, we could simultaneously estimate the transmission heterogeneity and the reproduction number from incidence time series. We validated the model with data of both simulated and real outbreaks. Our estimates of the overall and real-time heterogeneities of the six epidemics were consistent with those presented in the literature. Additionally, our model is robust to the ubiquitous bias of under-reporting and misspecification of serial interval. By analyzing recent data from South Africa, we found evidence that the Omicron might be of more significant transmission heterogeneity than Delta. Our model based on incidence data was proved to be reliable in estimating the real-time transmission heterogeneity.

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Superspreading potential of infection seeded by the SARS-CoV-2 Omicron BA.1 variant in South Korea

Cited by 9 publications

References 8 publications

Differences in the superspreading potentials of COVID-19 across contact settings

Differences in the superspreading potentials of COVID-19 across contact settings

SARS-CoV-2 viral load and shedding kinetics

Monitoring real-time transmission heterogeneity from incidence data

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