Dataset Information

A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events.

ABSTRACT: Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification of potentially linked cases of COVID-19 infection designed for clinical settings. Our method combines knowledge about infection dynamics, data describing the movements of individuals, and evolutionary analysis of genome sequences to assess whether data collected from cases of infection are consistent or inconsistent with linkage via direct transmission. A retrospective analysis of data from two wards at Cambridge University Hospitals NHS Foundation Trust during the first wave of the pandemic showed qualitatively different patterns of linkage between cases on designated COVID-19 and non-COVID-19 wards. The subsequent real-time application of our method to data from the second epidemic wave highlights its value for monitoring cases of infection in a clinical context.

SUBMITTER: Illingworth CJR

PROVIDER: S-EPMC8892943 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events.

Illingworth Christopher J R CJR Hamilton William L WL Jackson Christopher C Warne Ben B Popay Ashley A Meredith Luke L Hosmillo Myra M Jahun Aminu A Fieldman Tom T Routledge Matthew M Houldcroft Charlotte J CJ Caller Laura L Caddy Sarah S Yakovleva Anna A Hall Grant G Khokhar Fahad A FA Feltwell Theresa T Pinckert Malte L ML Georgana Iliana I Chaudhry Yasmin Y Curran Martin M Parmar Surendra S Sparkes Dominic D Rivett Lucy L Jones Nick K NK Sridhar Sushmita S Forrest Sally S Dymond Tom T Grainger Kayleigh K Workman Chris C Gkrania-Klotsas Effrossyni E Brown Nicholas M NM Weekes Michael P MP Baker Stephen S Peacock Sharon J SJ Gouliouris Theodore T Goodfellow Ian I Angelis Daniela De D Török M Estée ME

Molecular biology and evolution 20220301 3

Identifying linked cases of infection is a critical component of the public health response to viral infectious diseases. In a clinical context, there is a need to make rapid assessments of whether cases of infection have arrived independently onto a ward, or are potentially linked via direct transmission. Viral genome sequence data are of great value in making these assessments, but are often not the only form of data available. Here, we describe A2B-COVID, a method for the rapid identification ...[more]

PMID: 35106603

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Project description:ImportanceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiology of coronavirus disease 2019 (COVID-19), is readily transmitted person to person. Optimal control of COVID-19 depends on directing resources and health messaging to mitigation efforts that are most likely to prevent transmission, but the relative importance of such measures has been disputed.ObjectiveTo assess the proportion of SARS-CoV-2 transmissions in the community that likely occur from persons without symptoms.Design, setting, and participantsThis decision analytical model assessed the relative amount of transmission from presymptomatic, never symptomatic, and symptomatic individuals across a range of scenarios in which the proportion of transmission from people who never develop symptoms (ie, remain asymptomatic) and the infectious period were varied according to published best estimates. For all estimates, data from a meta-analysis was used to set the incubation period at a median of 5 days. The infectious period duration was maintained at 10 days, and peak infectiousness was varied between 3 and 7 days (-2 and +2 days relative to the median incubation period). The overall proportion of SARS-CoV-2 was varied between 0% and 70% to assess a wide range of possible proportions.Main outcomes and measuresLevel of transmission of SARS-CoV-2 from presymptomatic, never symptomatic, and symptomatic individuals.ResultsThe baseline assumptions for the model were that peak infectiousness occurred at the median of symptom onset and that 30% of individuals with infection never develop symptoms and are 75% as infectious as those who do develop symptoms. Combined, these baseline assumptions imply that persons with infection who never develop symptoms may account for approximately 24% of all transmission. In this base case, 59% of all transmission came from asymptomatic transmission, comprising 35% from presymptomatic individuals and 24% from individuals who never develop symptoms. Under a broad range of values for each of these assumptions, at least 50% of new SARS-CoV-2 infections was estimated to have originated from exposure to individuals with infection but without symptoms.Conclusions and relevanceIn this decision analytical model of multiple scenarios of proportions of asymptomatic individuals with COVID-19 and infectious periods, transmission from asymptomatic individuals was estimated to account for more than half of all transmissions. In addition to identification and isolation of persons with symptomatic COVID-19, effective control of spread will require reducing the risk of transmission from people with infection who do not have symptoms. These findings suggest that measures such as wearing masks, hand hygiene, social distancing, and strategic testing of people who are not ill will be foundational to slowing the spread of COVID-19 until safe and effective vaccines are available and widely used.

Dataset Information

A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events.

Publications

A2B-COVID: A Tool for Rapidly Evaluating Potential SARS-CoV-2 Transmission Events.

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