Serial Interval and Generation Interval for Imported and Local Infectors, Respectively, Estimated Using Reported Contact-Tracing Data of COVID-19 in China

Li, Menghui; Li, Kai; Song, Yukun; Ming, Wang; Wu, Jinshan

doi:10.3389/fpubh.2020.577431

Cited by 22 publications

(23 citation statements)

References 26 publications

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“…For the latter SI, we report a mean of 4.8 days, median of 4.0 days, IQR between 3.0 and 7.0 days, 95% centile from 1.0 to 9.5 days, 95% percentile of 9.0 days, and a range from 1.0 to 10.0 days. We observe that the mean (and median) SI decreases when generation increases, and this finding was reported previously in ( Li et al, 2020a , Ma et al, 2020 ). With the sample means, we calculate the ratio of latter SI over former SI at (4.8 / 5.4 =) 0.89, which is roughly the same scale as 0.73 in ( Ma et al, 2020 ) and 0.94 or 0.75 in ( Li et al, 2020a ).…”

Section: Resultssupporting

confidence: 91%

“…We observe that the mean (and median) SI decreases when generation increases, and this finding was reported previously in ( Li et al, 2020a , Ma et al, 2020 ). With the sample means, we calculate the ratio of latter SI over former SI at (4.8 / 5.4 =) 0.89, which is roughly the same scale as 0.73 in ( Ma et al, 2020 ) and 0.94 or 0.75 in ( Li et al, 2020a ). Empirically, the pairwise difference of latter SI minus former SI has a mean of −0.7 days, median of 0.0 day, and IQR between −3.0 and 2.0 days.…”

Section: Resultssupporting

confidence: 91%

“…In other words, SI was considered as a fixed distribution across transmission generations. However, two recent studies reported that SI appears with slight discrepancies across different transmission generations according to the summary statistics at populational scale ( Li et al, 2020a , Ma et al, 2020 ). Inspiring by their findings, we suspect there may exist a solid difference in the mean SI in consecutive generations in a transmission chain.…”

Section: Introductionmentioning

confidence: 99%

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Shrinkage in serial intervals across transmission generations of COVID-19

Zhao

Tang

et al. 2021

Journal of Theoretical Biology

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One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within a cluster of cases. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.

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Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Shrinkage in serial intervals across transmission generations of COVID-19

Zhao

Tang

et al. 2021

Journal of Theoretical Biology

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“…The means of the jointly estimated generation interval and incubation period are in line with those reported elsewhere 6–8,10 with the mean generation interval reported here—4.3 days—falling in the range of 2.8–7.5 days previously reported (see Supplementary Table 1 and Extended Data Fig. 4).…”

Section: Discussionsupporting

confidence: 91%

Correlation between times to SARS-CoV-2 symptom onset and secondary transmission undermines epidemic control efforts

Linton

Akhmetzhanov

Nishiura

2021

Preprint

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Severe acute respiratory coronavirus 2 (SARS-CoV-2) infections have been associated with substantial presymptomatic transmission, which occurs when the generation interval--the time between infection of an individual with a pathogen and transmission of the pathogen to another individual--is shorter than the incubation period--the time between infection and symptom onset. We collected a dataset of 257 SARS-CoV-2 transmission pairs in Japan and jointly estimated the mean generation interval (3.7-5.1 days) and mean incubation period (4.4-5.7 days) as well as measured their dependence (Kendall's tau of 0.4-0.6), taking into consideration demographic and epidemiological characteristics of the pairs. The positive correlation between the two parameters demonstrates that reliance on isolation of symptomatic COVID-19 cases as a focal point of control efforts is insufficient to address the challenges posed by SARS-CoV-2 transmission dynamics. Accounting for this dependence within SARS-CoV-2 epidemic models can also improve model estimates.

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“…2). Because there is no consensus in the literature as to the precise serial interval for each variant, we used an uncertain serial interval with mean of 5.2 days and standard deviation of 4 days [31][32][33] . Through the uncertain serial interval, multiple distributions were explored where the mean was allowed to vary from 2.2 to 8.2 days, and the standard deviation varied from 2.5 to 5.5 days.…”

Section: Effective Reproduction Numbermentioning

confidence: 99%

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 lineages

Petrone

Rothman

Breban

et al. 2021

Preprint

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Emerging SARS-CoV-2 variants have shaped the second year of the COVID-19 pandemic and the public health discourse around effective control measures. Evaluating the public health threat posed by a new variant is essential for appropriately adapting response efforts when community transmission is detected. However, this assessment requires that a true comparison can be made between the new variant and its predecessors because factors other than the virus genotype may influence spread and transmission. In this study, we develop a framework that integrates genomic surveillance data to estimate the relative effective reproduction number (Rt) of co-circulating lineages. We use Connecticut, a state in the northeastern United States in which the SARS-CoV-2 variants B.1.1.7 and B.1.526 co-circulated in early 2021, as a case study for implementing this framework. We find that the Rt of B.1.1.7 was 6-10% larger than that of B.1.526 in Connecticut in the midst of a COVID-19 vaccination campaign. To assess the generalizability of this framework, we apply it to genomic surveillance data from New York City and observe the same trend. Finally, we use discrete phylogeography to demonstrate that while both variants were introduced into Connecticut at comparable frequencies, clades that resulted from introductions of B.1.1.7 were larger than those resulting from B.1.526 introductions. Our framework, which uses open-source methods requiring minimal computational resources, may be used to monitor near real-time variant dynamics in a myriad of settings.

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Serial Interval and Generation Interval for Imported and Local Infectors, Respectively, Estimated Using Reported Contact-Tracing Data of COVID-19 in China

Cited by 22 publications

References 26 publications

Shrinkage in serial intervals across transmission generations of COVID-19

Shrinkage in serial intervals across transmission generations of COVID-19

Correlation between times to SARS-CoV-2 symptom onset and secondary transmission undermines epidemic control efforts

Combining genomic and epidemiological data to compare the transmissibility of SARS-CoV-2 lineages

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