Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

Ferretti, Luca; Wymant, Chris; Kendall, Michaela; Zhao, Lele; Nurtay, Anel; Abeler-Dörner, Lucie; Parker, Michael; Bonsall, David; Fraser, Christophe

doi:10.1126/science.abb6936

Cited by 2,418 publications

(2,888 citation statements)

References 40 publications

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“…This analysis also has implications for contact tracing [5], because it shows that the people for whom this is most important are those who interact most strongly. Thus if it can be made to work efficiently on this group alone, it could help to limit the effect of the highly active groups on the overall statistics.…”

Section: Contact Tracing Testing and Immunisationmentioning

confidence: 99%

Human agency and infection rates: implications for social distancing during epidemics

Ramsey

2020

Preprint

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Social distancing is an important measure in controlling epidemics. This paper presents a simple theoretical model focussed on the implications of the wide range in interaction rates between individuals, both within the workplace and in the social setting. The model is based on well-mixed populations and so is not intended for studying geographic spread. The model shows that epidemic growth rate is largely determined by the upper interactivity quantiles of society, implying that the most efficient methods of epidemic control are interaction capping approaches rather than overall reductions in interaction.The theoretical model can also be applied to look at the dynamics of epidemic progression under various scenarios. The theoretical model suggests that with no intervention herd immunity would be achieved with a lower overall infection rate than if variation in interaction rate is ignored, because by this stage almost all the most interactive members of society would have had the infection; however the overall mortality with such an approach is very high. Scenarios for mitigation and suppression suggest that, by using interactivity capping, it should be possible to control an epidemic without extreme sanctions on the majority of the population if R0 of the uncontrolled infection is 2.4. However to control the infection rate to a specific level will always require the switching on and off of measures and for this reason eradication is likely to be a less costly policy in the long run. While social distancing alone can be used for eradication it is not be a good mechanism on its own to prevent reinfection. The use of robust testing, quarantining, and contact tracing would strengthen any social distancing measures, speed up eradication, and be a better tool for prevention of infection or reinfection.

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Section: Contact Tracing Testing and Immunisationmentioning

confidence: 99%

Human agency and infection rates: implications for social distancing during epidemics

Ramsey

2020

Preprint

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“…8 A recent proposal of a contact tracing mobile phone App could allow for this instant contact tracing, decreasing the time to isolation for symptomatic contacts. 9 Our framework enables comparison of active monitoring and individual quarantine and considers parameters such as delays, and imperfect isolation to account for known transmission of this respiratory virus after isolation in a healthcare setting. 10 One of the key uncertainties surrounding COVID-19 is the extent of asymptomatic and presymptomatic transmission.…”

Section: Introductionmentioning

confidence: 99%

Comparative Impact of Individual Quarantine vs. Active Monitoring of Contacts for the Mitigation of COVID-19: a modelling study

Peak

Kahn

Grad

et al. 2020

Preprint

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Individual quarantine and active monitoring of contacts are core disease control strategies, particularly for emerging infectious diseases such as Coronavirus Disease 2019 . To estimate the comparative efficacy of these interventions to control COVID-19, we fit a stochastic branching model, comparing two sets of reported parameters for the dynamics of the disease. Our results suggest that individual quarantine may contain an outbreak of COVID-19 with a short serial interval (4.8 days) only in settings with high intervention performance where at least three-quarters of infected contacts are individually quarantined. However, in settings where this performance is unrealistically high and the outbreak of COVID-19 continues to grow, so too will the burden of the number of contacts traced for active monitoring or quarantine. In such circumstances where resources are prioritized for scalable interventions such as social distancing, we show active monitoring or individual quarantine of high-risk contacts can contribute synergistically to social distancing. To the extent that interventions based on contact tracing can be implemented, therefore, they can help mitigate the spread of COVID-19. Our model highlights the urgent need for more data on the serial interval and the extent of presymptomatic transmission in order to make data-driven policy decisions regarding the cost-benefit comparisons of individual quarantine vs. active monitoring of contacts.

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“…Because most infections in our model come from pre-symptomatic people or from people who never show symptoms, it may be impossible to prevent most infections with test-and-isolation policies unless non-symptomatic people are also isolated (e.g., quarantine of families or testing non-symptomatic people). The testing and isolation simulation results are particularly sensitive to assumptions about asymptomatic and pre-symptomatic transmission [16], neither of which is yet well-characterized for SARS-CoV-2. Another modeling study found testing with isolation to be more effective, but their model assumed less asymptomatic transmission than Corvid does currently [17].…”

Section: Discussionmentioning

confidence: 99%

Modeling layered non-pharmaceutical interventions against SARS-CoV-2 in the United States with Corvid

Chao

Oron

Srikrishna

2020

Preprint

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Background: The novel coronavirus SARS-CoV-2 has rapidly spread across the globe and is poised to cause millions of deaths worldwide. There are currently no proven pharmaceutical treatments, and vaccines are likely over a year away. At present, non-pharmaceutical interventions (NPIs) are the only effective option to reduce transmission of the virus, but it is not clear how to deploy these potentially expensive and disruptive measures. Modeling can be used to understand the potential effectiveness of NPIs for both suppression and mitigation efforts. Methods and Findings: We developed Corvid, an adaptation of the agent-based influenza model called FluTE to SARS-CoV-2 transmission. To demonstrate features of the model relevant for studying the effects of NPIs, we simulated transmission of SARS-CoV-2 in a synthetic population representing a metropolitan area in the United States. Transmission in the model occurs in several settings, including at home, at work, and in schools. We simulated several combinations of NPIs that targeted transmission in these settings, such as school closures and work-from-home policies. We also simulated three strategies for testing and isolating symptomatic cases. For our demonstration parameters, we show that testing followed by home isolation of ascertained cases reduced transmission by a modest amount. We also show how further reductions may follow by isolating cases in safe facilities away from susceptible family members or by quarantining all family members to prevent transmission from likely infections that have yet to manifest. Conclusions: Models that explicitly include settings where individuals interact such as the home, work, and school are useful for studying the effectiveness of NPIs, as these are more dependent on community structure than pharmaceutical interventions such as vaccination. Corvid can be used to help evaluate complex combinations of interventions, although there is no substitute for real-world observations. Our results on NPI effectiveness summarize the behavior of the model for an assumed set of parameters for demonstration purposes. Model results can be sensitive to the assumptions made about disease transmission and the natural history of the disease, both of which are not yet sufficiently characterized for SARS-CoV-2 for quantitative modeling. Models of SARS-CoV-2 transmission will need to be updated as the pathogen becomes better-understood.

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Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing

Cited by 2,418 publications

References 40 publications

Human agency and infection rates: implications for social distancing during epidemics

Human agency and infection rates: implications for social distancing during epidemics

Comparative Impact of Individual Quarantine vs. Active Monitoring of Contacts for the Mitigation of COVID-19: a modelling study

Modeling layered non-pharmaceutical interventions against SARS-CoV-2 in the United States with Corvid

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