Exploration of Superspreading Events in 2015 MERS-CoV Outbreak in Korea by Branching Process Models

Choe, Seoyun; Kim, Hee‐Sung; Lee, Sun-Mi

doi:10.3390/ijerph17176137

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…The characteristics of the selected articles are listed in Supplementary Table S3 . Among the 60 estimates of transmission heterogeneity, four (6.7%) from two studies estimated the dispersion parameter ( k ) of SARS [16] , [18] , 11 (18.3%) from eight articles estimated the k for MERS [16] , [17] , [30] , [31] , [32] , [33] , [34] , [35] , and 45 (75.0%) from 17 articles estimated the k for COVID-19 [14] , [15] , [19] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] . Forty estimates (66.7%) were based on transmission pair data (i.e., offspring case number generated by each index case), and four estimates were calculated using epidemic/cluster size data.…”

Section: Resultsmentioning

confidence: 99%

“…In some studies, standard compartment models (e.g., the classic susceptible-infectious-recovered model) were combined with superspreading compartments to characterize the transmission heterogeneity of SARS [12] and MERS outbreaks [13] . In contrast, some studies [14] , [15] , [16] , [17] fitted the observed epidemiological data by a negative binomial (NB) distribution to quantify the superspreading potential. This approach was initially proposed by Lloyd-Smith et al [18] , in which the heterogeneity of infectiousness among individuals was quantified by estimating the dispersion parameter ( k ).…”

Section: Introductionmentioning

confidence: 99%

“…Early studies evaluated the superspreading potential and transmission heterogeneity of coronaviruses during different periods and under different intervention measures [17] , [25] , [26] . However, a comprehensive summary of the superspreading potential of the three coronavirus diseases is lacking, and few studies have compared their transmission dispersiveness.…”

Section: Introductionmentioning

confidence: 99%

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Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

Wang

Chen

Guo

et al. 2021

Computational and Structural Biotechnology Journal

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Background Severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and coronavirus disease 2019 (COVID-19) have caused substantial public health burdens and global health threats. Understanding the superspreading potential of a virus is important for characterizing transmission patterns and informing strategic decision-making in disease control. This systematic review aimed to summarize the existing evidence on superspreading features and to compare the heterogeneity in transmission within and among various coronavirus epidemics of SARS, MERS and COVID-19. Methods PubMed, Medline, and Embase databases were extensively searched for original studies on the transmission heterogeneity of SARS, MERS, and COVID-19 published in English between January 1, 2003, and February 10, 2021. After screening the articles, we extracted data pertaining to the estimated dispersion parameter ( k ) which has been a commonly-used measurement for transmission heterogeneity and superspreading potential. Findings We included a total of 60 estimates of transmission heterogeneity from 26 studies on outbreaks in 22 regions. The majority (90%) of the k estimates were small, with values less than 1, indicating an over-dispersed transmission pattern. The point estimates of k for SARS and MERS ranged from 0.12 to 0.20 and from 0.06 to 2.94, respectively. Among 45 estimates of individual-level transmission heterogeneity for COVID-19 from 17 articles, 91% were derived from Asian regions. The point estimates of k for COVID-19 ranged between 0.1 and 5.0. Conclusions We detected a substantial over-dispersed transmission pattern in all three coronaviruses, while the k estimates varied by differences in study design and public health capacity. Our findings suggested that even with a reduced R value, the epidemic still has a high resurgence potential due to transmission heterogeneity.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

Wang

Chen

Guo

et al. 2021

Computational and Structural Biotechnology Journal

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“…This clearly showed super-spreading events (the five super-spreaders) in generating secondary cases due to various complex factors. More detailed epidemiological data on the 2015 MERS-CoV cases can be found in the previous studies [23,24].…”

Section: Generating the Simulation Space Based On The Empirical Contamentioning

confidence: 99%

Effectiveness of Intervention Strategies on MERS-CoV Transmission Dynamics in South Korea, 2015: Simulations on the Network Based on the Real-World Contact Data

Kim

Ryu

Lee

2021

IJERPH

Self Cite

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The MERS-CoV spread in South Korea in 2015 was not only the largest outbreak of MERS-CoV in the region other than the Middle East but also a historic epidemic in South Korea. Thus, investigation of the MERS-CoV transmission dynamics, especially by agent-based modeling, would be meaningful for devising intervention strategies for novel infectious diseases. In this study, an agent-based model on MERS-CoV transmission in South Korea in 2015 was built and analyzed. The prominent characteristic of this model was that it built the simulation environment based on the real-world contact tracing network, which can be characterized as being scale-free. In the simulations, we explored the effectiveness of three possible intervention scenarios; mass quarantine, isolation, and isolation combined with acquaintance quarantine. The differences in MERS-CoV transmission dynamics by the number of links of the index case agent were examined. The simulation results indicate that isolation combined with acquaintance quarantine is more effective than others, and they also suggest the key role of super-spreaders in MERS-CoV transmission.

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“…The infection spread is largely due to superspreaders in the community who transmit the infection to several individuals, [18] these cases can only be prevented by detailed network analysis. [19] Thus, it is very important to post-process the contact tracing data, and make strategies for testing and isolating individuals at the risk. Policymakers should have strategies to acquire maximum spread information using minimum tests conducted in the community.…”

Section: Introductionmentioning

confidence: 99%

Covid-19 Risk Evaluation and Testing Strategies Based on Contact Tracing Network and Information Analysis

Kumar

2020

Preprint

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Contact tracing and efficient testing can have an imperative part in containing the COVID-19 spread, with minimal social and economic disruption. Testing serves many purposes: isolating the COVID-19 positive tested individuals, identifying the contacts at the risk, and locating the hotspots and safe zones for administrative planning. However, it is a challenging task to identify the right individuals for the test in view of the high COVID -19 spread, a large number of presymptomatic and asymptomatic cases, and limited testing capabilities. The individuals for COVID -19 are identified based on direct-contact, travel history, and symptoms, which are more individualized and do not explicitly include a group risk assessment, and in turn, do not preclude the transmission from the superspreaders. Policymakers need to limit testing in the shortage of test resources, and focus on gaining the most information from the tests performed. In this work, we introduce a protocol for the identification of the group of individuals to be tested for acquiring maximum risk information of a community with minimum individual tests. Firstly, an algorithm is proposed to determine the risk profile of all the individuals in the community by incorporating serial and parallel pathways of the infection transmission considering multiple steps of transmission. Next, we consider several potential groups that could be tested from the community, and analyze them one by one for their comparison. In a group, few individuals can be positive, and the remaining few can be negative, generating sets of several test-outcomes with unequal probability. The protocol involves the probability calculation and reassessment of the network's risk profile in all the test output cases. Finally, the best group is identified in all the groups studied, in which risk profiles between post and pre-test are maximally different. The analysis shows that in general, information increases with an increase in the group size. Notably, a strategically chosen small group may provide more information from the test results, than a standard larger group. The proposed systematic strategy would help in the selection of the right individuals for the testing, and in extracting far more information from the minimum samples, to effectively aid the epidemic mitigation. The protocol is generic, and can also be applied to any other epidemic spread in the future.

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Exploration of Superspreading Events in 2015 MERS-CoV Outbreak in Korea by Branching Process Models

Cited by 9 publications

References 24 publications

Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

Superspreading and heterogeneity in transmission of SARS, MERS, and COVID-19: A systematic review

Effectiveness of Intervention Strategies on MERS-CoV Transmission Dynamics in South Korea, 2015: Simulations on the Network Based on the Real-World Contact Data

Covid-19 Risk Evaluation and Testing Strategies Based on Contact Tracing Network and Information Analysis

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