Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020

Singanayagam, Anika; Patel, Monika; Charlett, André; Bernal, Jamie Lopez; Saliba, Vanessa; Ellis, Joanna; Ladhani, Shamez; Zambon, Maria; Gopal, Robin

doi:10.2807/1560-7917.es.2020.25.32.2001483

Cited by 836 publications

(934 citation statements)

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“…Still, we anticipate that the central patterns will hold across populations: first, that low Cts (<30) strongly predict acute infection, and second, that a follow-up test collected within two days of an initial positive test can substantially help to discern whether a person is closer to the beginning or the end of their infection. Our study did not test for the presence of infectious virus, though previous studies have documented a close inverse correlation between Ct values and culturable virus (14). To manage the spread of SARS-CoV-2, we must develop novel technologies and find new ways to extract more value from the tools that are already available.…”

Section: Main Textmentioning

confidence: 99%

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Viral dynamics of acute SARS-CoV-2 infection

Kissler

Fauver

Mack

et al. 2020

Preprint

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SARS-CoV-2 diagnostics that report viral RNA concentrations can be used to determine a patient's stage of infection, but this potential has not yet been realized due to a lack of prospective longitudinal data to calibrate such inferences. Here, we report the viral RNA trajectories for 68 individuals using quantitative PCR testing. On average, symptomatic and asymptomatic individuals reached similar peak viral RNA concentrations (22.2 Ct, 95% credible interval [19.1, 25.1] vs. 22.4 Ct [20.2, 24.5]) within similar amounts of time (2.9 days [0.7, 4.7] vs. 3.0 days [1.3, 4.3]), but acute shedding lasted longer for symptomatic individuals (10.5 days [6.5, 14.0] vs. 6.7 days [3.2, 9.2]). A second test within 2 days after an initial positive PCR result reliably indicated whether viral RNA concentration was increasing, decreasing, or in a low-level persistent phase. Quantitative viral RNA assessment, informed by viral trajectory, can improve algorithms for clinical and public health management.

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Section: Main Textmentioning

confidence: 99%

“…Of the individuals included in the study, 27 of the 46 with active infections and 40 of the 68 overall were from staff and vendors. The median number of tests administered to each of the 68 individuals was 41 (IQR [14,51]; Range [5,70]). The median number of positive Ct values recorded for each person was 3 (IQR [2,4]; Range [1,9]).…”

Section: Main Textmentioning

confidence: 99%

Viral dynamics of acute SARS-CoV-2 infection

Kissler

Fauver

Mack

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…The software allows full flexibility with regards to parameter choices, that, for example, determine the time-course of infection, the proportion of asymptomatic cases and the test sensitivity, etc., and can thus be tailored to user-specific queries. We have however carefully calibrated the models' default parameters to reproduce published and inhouse clinical data on the incubation time 53 , the off-set of infectiousness after peak virus load/symptom onset [54][55][56]59 , as well as the time-dependent test sensitivities 57,58 . Figure 2A shows the cumulative time-to-symptom-onset (grey shaded area) compiled in a meta-analysis of 56 studies 53 , together with the model-predictions (solid-and dashed lines) using the default parameters.…”

Section: Resultsmentioning

confidence: 99%

“…The software allows full flexibility with regards to parameter choices, that, for example, determine the time-course of infection, the proportion of asymptomatic cases and the test sensitivity and much more. However, a set of default parameters is provided, that has been carefully derived by fitting the model's parameters to available clinical and in-house data on the incubation time 53 , the off-set of infectiousness after symptom onset or peak virus load [54][55][56] , as well as the time-dependent test sensitivities 57,58 . Details on the parameter fitting procedure and analysis of infectivity profiles are provided in Supplementary Note 2-3.…”

Section: Methodsmentioning

confidence: 99%

COVIDStrategyCalculator: A software to assess testing- and quarantine strategies for incoming travelers, contact person management and de-isolation

Toorn

Bourquain

et al. 2020

Preprint

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In early 2020 COVID-19 turned into a global pandemic. Non-pharmaceutical interventions (NPIs), including the isolation of infected individuals, tracing and quarantine of exposed individuals are decisive tools to prevent onwards transmission and curb fatalities. Strategies that combine NPIs with SARS-CoV-2 testing may help to shorten quarantine durations while being non-inferior with respect to infection prevention. Thus, combined strategies can help reducing the socio-economic burden of SARS-CoV-2 and increase public acceptance.We developed a software that enables policy makers to calculate the reduction in transmissibility through quarantine or isolation in combination with arbitrary testing strategies. The user chooses between three different modi [(i) isolation of infected individuals, (ii) management of potentially infected contacts and (iii) quarantine of incoming travelers], while having flexibility in customizing testing strategies and model parameters. The software enables decision makers to tailor calculations specifically to their questions and perform an assessment ‘on the fly’, based on current evidence on infection dynamics.Underneath, we analytically solve a stochastic transit compartment model of the infection time course, which captures temporal changes in test sensitivities, incubation- and infectious periods, as well as times to symptom onset using its default parameters.Using default parameters, we estimated that testing travelers at the point of entry reduces the risk about 4.69 (4.19,4.83) fold for PCR vs. 3.59 (3.22, 3.69) fold for rapid diagnostic tests (RDT, 87% relative sensitivity) when combined with symptom screening. In comparison to 14 days of pure quarantine, 8 (PCR) vs. 10 (RDT) days of pre-test quarantine would be noninferior for incoming travelers as well as for contact person management. De-isolation of infected individuals 11 days after symptom onset reduces the risk by >99fold (7.68,>1012). This tool is freely available from: https://github.com/CovidStrategyCalculator/CovidStrategyCalculator

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“…A recent article showed the probability of culturing virus declines to 8% in samples with Ct > 35. [27] Only one study described a positive viral culture on samples with SARS-CoV-2 RNA on RT-PCR, suggesting that most samples do not contain enough infectious virus. Most sampling methods affect viral infectivity, which may partly explain these results.…”

Section: Discussionmentioning

confidence: 99%

Airborne contamination of COVID-19 in hospitals: a scoping review of the current evidence

Birgand

Peiffer-Smadja

Fournier

et al. 2020

Preprint

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Introduction A controversy remains worldwide regarding the transmission routes of SARS-CoV-2 in hospital settings. We reviewed the current evidence on the air contamination with SARS-CoV-2 in hospital settings, and the factors associated to the contamination including the viral load and the particles size. Methods The MEDLINE, Embase, Web of Science databases were systematically interrogated for original English-language articles detailing COVID-19 air contamination in hospital settings between 1 December 2019 and 21 July 2020. This study was conducted in accordance with the PRISMA-ScR guidelines. The positivity rate of SARS-CoV-2 viral RNA and culture were described and compared according to the setting, clinical context, air ventilation system, and distance from patient. The SARS-CoV-2 RNA concentrations in copies per m3 of air were pooled and their distribution were described by hospital areas. Particle sizes and SARS-CoV-2 RNA concentrations in copies or TCID50 per m3 were analysed after categorization of sizes in <1 micrometers, 1-4 micrometers, and >4 micrometers. Results Among 2,034 records identified, 17 articles were included in the review. Overall, 27.5% (68/247) of air sampled from close patients environment were positive for SARS-CoV-2 RNA, without difference according to the setting (ICU: 27/97, 27.8%; non-ICU: 41/150, 27.3%; p=0.93), the distance from patients (<1 meter: 1/64, 1.5%; 1-5 meters: 4/67, 6%; p=0.4). In other areas, the positivity rate was 23.8% (5/21) in toilets, 9.5% (20/221) in clinical areas, 12.4% (15/121) in staff areas, and 34.1% (14/41) in public areas. A total of 78 viral cultures were performed in three studies, and 3 (4%) were positive, all from close patients environment. The median SARS-CoV-2 RNA concentrations varied from 1.103 copies per m3 (IQR: 0.4.103-9.103) in clinical areas to 9.7.103 (5.1.103-14.3.103) in the air of toilets or bathrooms. The protective equipment removal and patients rooms had high concentrations/titre of SARS-CoV-2 with aerosol size distributions that showed peaks in the <1 micrometers region, and staff offices in the >4 micrometers region. Conclusion In hospital, the air near and away from COVID-19 patients is frequently contaminated with SARS-CoV-2 RNA, with however, rare proofs of their viability. High viral loads found in toilet/bathrooms, staff and public hallways suggests to carefully consider these areas.

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Duration of infectiousness and correlation with RT-PCR cycle threshold values in cases of COVID-19, England, January to May 2020

Cited by 836 publications

References 9 publications

Viral dynamics of acute SARS-CoV-2 infection

Viral dynamics of acute SARS-CoV-2 infection

COVIDStrategyCalculator: A software to assess testing- and quarantine strategies for incoming travelers, contact person management and de-isolation

Airborne contamination of COVID-19 in hospitals: a scoping review of the current evidence

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