Transmission onset distribution of COVID-19

Chun, June Young; Baek, Gyuseung; Kim, Yongdai

doi:10.1016/j.ijid.2020.07.075

Cited by 43 publications

(52 citation statements)

References 15 publications

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“…The published studies consisted of 28 research articles, 1 3–29 4 letters, 2 30–32 2 reports, 33 34 a brief communication, 35 3 accepted manuscripts 36–38 and 2 pre-proofs. 39 40 …”

Section: Resultsmentioning

confidence: 99%

“…This approach was also used by other authors, including Ali et al 33 and Xu et al 37 Prete et al 2 used a modelling approach and also fitted a normal distribution to the data. In some papers, including those of Chun et al , 39 He et al , 35 and Bao et al , 16 shifted lognormal or shifted gamma distributions were used to deal with negative values of the serial interval.…”

Section: Discussionmentioning

confidence: 99%

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Rapid review of available evidence on the serial interval and generation time of COVID-19

et al. 2020

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The serial interval is the time between symptom onsets in an infector–infectee pair. The generation time, also known as the generation interval, is the time between infection events in an infector–infectee pair. The serial interval and the generation time are key parameters for assessing the dynamics of a disease. A number of scientific papers reported information pertaining to the serial interval and/or generation time for COVID-19. Objective Conduct a review of available evidence to advise on appropriate parameter values for serial interval and generation time in national COVID-19 transmission models for Ireland and on methodological issues relating to those parameters. Methods We conducted a rapid review of the literature covering the period 1 January 2020 and 21 August 2020, following predefined eligibility criteria. Forty scientific papers met our inclusion criteria and were included in the review. Results The mean of the serial interval ranged from 3.03 to 7.6 days, based on 38 estimates, and the median from 1.0 to 6.0 days (based on 15 estimates). Only three estimates were provided for the mean of the generation time. These ranged from 3.95 to 5.20 days. One estimate of 5.0 days was provided for the median of the generation time. Discussion Estimates of the serial interval and the generation time are very dependent on the specific factors that apply at the time that the data are collected, including the level of social contact. Consequently, the estimates may not be entirely relevant to other environments. Therefore, local estimates should be obtained as soon as possible. Careful consideration should be given to the methodology that is used. Real-time estimations of the serial interval/generation time, allowing for variations over time, may provide more accurate estimates of reproduction numbers than using conventionally fixed serial interval/generation time distributions.

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“…The published studies consisted of 28 research articles, 1 3–29 4 letters, 2 30–32 2 reports, 33 34 a brief communication, 35 3 accepted manuscripts 36–38 and 2 pre-proofs. 39 40 …”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Rapid review of available evidence on the serial interval and generation time of COVID-19

et al. 2020

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“…5,11,15 On the other hand, while older age can be considered a risk factor for worse outcomes in COVID-19 infection, 16 and by close contact with patients with minimal symptoms. 1,17 It is also not clear whether pregnant women are more prone to contracting COVID-19 compared to the general population, although previous studies have stated that pregnant women are prone to infection due to their "immunosuppressed" state. 18,19 The risk of developing severe symptoms of COVID-19 during pregnancy is not clear 20 ; it seems that symptoms tend to be less severe than those of a typical influenza infection.…”

Section: Discussionmentioning

confidence: 99%

Clinical features of pregnant women in Iran who died due to COVID‐19

Moghadam

Dini

Nassiri

et al. 2020

Intl J Gynecology & Obste

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“…T he emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019 has precipitated a global pandemic with over 45 million cases and 1 million deaths 1 . Coronavirus disease 2019 (COVID- 19), the clinical syndrome caused by SARS-CoV-2, is characterized by peak viral load and transmissibility prior to or at the time of symptom onset [2][3][4][5][6][7][8] with a disease course that varies from asymptomatic infection to critical illness 9 . Defining the host response to SARS-CoV-2, as compared to other respiratory viruses, is fundamental to identifying mechanisms of pathogenicity and potential therapeutic targets.…”

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confidence: 99%

Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses

et al. 2020

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SARS-CoV-2 infection is characterized by peak viral load in the upper airway prior to or at the time of symptom onset, an unusual feature that has enabled widespread transmission of the virus and precipitated a global pandemic. How SARS-CoV-2 is able to achieve high titer in the absence of symptoms remains unclear. Here, we examine the upper airway host transcriptional response in patients with COVID-19 (n = 93), other viral (n = 41) or non-viral (n = 100) acute respiratory illnesses (ARIs). Compared with other viral ARIs, COVID-19 is characterized by a pronounced interferon response but attenuated activation of other innate immune pathways, including toll-like receptor, interleukin and chemokine signaling. The IL-1 and NLRP3 inflammasome pathways are markedly less responsive to SARS-CoV-2, commensurate with a signature of diminished neutrophil and macrophage recruitment. This pattern resembles previously described distinctions between symptomatic and asymptomatic viral infections and may partly explain the propensity for pre-symptomatic transmission in COVID-19. We further use machine learning to build 27-, 10- and 3-gene classifiers that differentiate COVID-19 from other ARIs with AUROCs of 0.981, 0.954 and 0.885, respectively. Classifier performance is stable across a wide range of viral load, suggesting utility in mitigating false positive or false negative results of direct SARS-CoV-2 tests.

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Transmission onset distribution of COVID-19

Cited by 43 publications

References 15 publications

Rapid review of available evidence on the serial interval and generation time of COVID-19

Rapid review of available evidence on the serial interval and generation time of COVID-19

Clinical features of pregnant women in Iran who died due to COVID‐19

Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses

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