Impact of delays on effectiveness of contact tracing strategies for COVID-19: a modelling study.
Mirjam Kretzschmar,Ganna Rozhnova,Ganna Rozhnova,Martin C. J. Bootsma,Martin C. J. Bootsma,Michiel van Boven,Janneke van de Wijgert,Janneke van de Wijgert,Marc J. M. Bonten,Marc J. M. Bonten +9 more
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TLDR
The impact of timeliness and completeness in various steps of a contact tracing strategy is evaluated using a stochastic mathematical model with explicit time delays between time of infection and symptom onset and between symptom onset, diagnosis by testing, and isolation.Abstract:
Summary Background In countries with declining numbers of confirmed cases of COVID-19, lockdown measures are gradually being lifted. However, even if most physical distancing measures are continued, other public health measures will be needed to control the epidemic. Contact tracing via conventional methods or mobile app technology is central to control strategies during de-escalation of physical distancing. We aimed to identify key factors for a contact tracing strategy to be successful. Methods We evaluated the impact of timeliness and completeness in various steps of a contact tracing strategy using a stochastic mathematical model with explicit time delays between time of infection and symptom onset, and between symptom onset, diagnosis by testing, and isolation (testing delay). The model also includes tracing of close contacts (eg, household members) and casual contacts, followed by testing regardless of symptoms and isolation if testing positive, with different tracing delays and coverages. We computed effective reproduction numbers of a contact tracing strategy (RCTS) for a population with physical distancing measures and various scenarios for isolation of index cases and tracing and quarantine of their contacts. Findings For the most optimistic scenario (testing and tracing delays of 0 days and tracing coverage of 100%), and assuming that around 40% of transmissions occur before symptom onset, the model predicts that the estimated effective reproduction number of 1·2 (with physical distancing only) will be reduced to 0·8 (95% CI 0·7–0·9) by adding contact tracing. The model also shows that a similar reduction can be achieved when testing and tracing coverage is reduced to 80% (RCTS 0·8, 95% CI 0·7–1·0). A testing delay of more than 1 day requires the tracing delay to be at most 1 day or tracing coverage to be at least 80% to keep RCTS below 1. With a testing delay of 3 days or longer, even the most efficient strategy cannot reach RCTS values below 1. The effect of minimising tracing delay (eg, with app-based technology) declines with decreasing coverage of app use, but app-based tracing alone remains more effective than conventional tracing alone even with 20% coverage, reducing the reproduction number by 17·6% compared with 2·5%. The proportion of onward transmissions per index case that can be prevented depends on testing and tracing delays, and given a 0-day tracing delay, ranges from up to 79·9% with a 0-day testing delay to 41·8% with a 3-day testing delay and 4·9% with a 7-day testing delay. Interpretation In our model, minimising testing delay had the largest impact on reducing onward transmissions. Optimising testing and tracing coverage and minimising tracing delays, for instance with app-based technology, further enhanced contact tracing effectiveness, with the potential to prevent up to 80% of all transmissions. Access to testing should therefore be optimised, and mobile app technology might reduce delays in the contact tracing process and optimise contact tracing coverage. Funding ZonMw, Fundacao para a Ciencia e a Tecnologia, and EU Horizon 2020 RECOVER.read more
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SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis
Muge Cevik,Muge Cevik,Matthew Tate,Ollie Lloyd,Ollie Lloyd,Alberto Enrico Maraolo,Jenna Schafers,Antonia Ho +7 more
TL;DR: Although SARS-CoV-2 RNA shedding in respiratory and stool samples can be prolonged, duration of viable virus is relatively short-lived.
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Rapid, point-of-care antigen and molecular-based tests for diagnosis of SARS-CoV-2 infection.
Jacqueline Dinnes,Jacqueline Dinnes,Jonathan J Deeks,Ada Adriano,Sarah Berhane,Clare Davenport,Sabine Dittrich,Devy Emperador,Yemisi Takwoingi,Jane Cunningham,Sophie Beese,Janine Dretzke,Lavinia Ferrante di Ruffano,Isobel Marion Harris,Malcolm J Price,Sian Taylor-Phillips,Lotty Hooft,Mariska M.G. Leeflang,René Spijker,Ann Van den Bruel +19 more
TL;DR: Assessment of the diagnostic accuracy of point‐of‐care antigen and molecular‐based tests to determine if a person presenting in the community or in primary or secondary care has current SARS‐CoV‐2 infection found no studies at low risk of bias for all quality domains and concerns about applicability of results across all studies.
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The temporal association of introducing and lifting non-pharmaceutical interventions with the time-varying reproduction number (R) of SARS-CoV-2: a modelling study across 131 countries.
TL;DR: The association of introducing and lifting NPIs with the level of transmission of SARS-CoV-2, as measured by the time-varying reproduction number (R), is understood from a broad perspective across 131 countries.
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Covasim: an agent-based model of COVID-19 dynamics and interventions
Cliff C. Kerr,Robyn M. Stuart,Robyn M. Stuart,Dina Mistry,Romesh G. Abeysuriya,Gregory R. Hart,Katherine Rosenfeld,Prashanth Selvaraj,Rafael C. Núñez,Brittany Hagedorn,Lauren George,Amanda S Izzo,Anna Palmer,Dominic Delport,Carrie Bennette,Bradley G. Wagner,Stewart T. Chang,Jamie A. Cohen,Jasmina Panovska-Griffiths,Michał Jastrzębski,Assaf P. Oron,Edward Allen Wenger,Michael Famulare,Daniel J. Klein +23 more
TL;DR: The methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address the urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs, is described.
Journal ArticleDOI
Covasim: An agent-based model of COVID-19 dynamics and interventions.
Cliff C. Kerr,Robyn M. Stuart,Robyn M. Stuart,Dina Mistry,Romesh G. Abeysuriya,Katherine Rosenfeld,Gregory R. Hart,Rafael C. Núñez,Jamie A. Cohen,Prashanth Selvaraj,Brittany Hagedorn,Lauren George,Michał Jastrzębski,Amanda S Izzo,Greer Fowler,Anna Palmer,Dominic Delport,Nick Scott,Sherrie L Kelly,Caroline S Bennette,Bradley G. Wagner,Stewart T. Chang,Assaf P. Oron,Edward A. Wenger,Jasmina Panovska-Griffiths,Jasmina Panovska-Griffiths,Michael Famulare,Daniel J. Klein +27 more
TL;DR: Covasim as discussed by the authors is an agent-based simulation model developed to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.
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TL;DR: This study provides the first large-scale quantitative approach to contact patterns relevant for infections transmitted by the respiratory or close-contact route, and the results should lead to improved parameterisation of mathematical models used to design control strategies.
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