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The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19.

ABSTRACT: The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.

SUBMITTER: Willem L

PROVIDER: S-EPMC7943552 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19.

Willem Lander L Abrams Steven S Libin Pieter J K PJK Coletti Pietro P Kuylen Elise E Petrof Oana O Møgelmose Signe S Wambua James J Herzog Sereina A SA Faes Christel C Beutels Philippe P Hens Niel N

Nature communications 20210309 1

The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and fre ...[more]

PMID: 33750778

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Project description:BackgroundIn 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.MethodsWe 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.FindingsFor 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.InterpretationIn 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.FundingZonMw, Fundação para a Ciência e a Tecnologia, and EU Horizon 2020 RECOVER.

Project description:Digital contact tracing (DCT) applications have been introduced in many countries to aid the containment of COVID-19 outbreaks. Initially, enthusiasm was high regarding their implementation as a non-pharmaceutical intervention (NPI). However, no country was able to prevent larger outbreaks without falling back to harsher NPIs. Here, we discuss results of a stochastic infectious-disease model that provide insights in how the progression of an outbreak and key parameters such as detection probability, app participation and its distribution, as well as engagement of users impact DCT efficacy informed by results of empirical studies. We further show how contact heterogeneity and local contact clustering impact the intervention’s efficacy. We conclude that DCT apps might have prevented cases on the order of single-digit percentages during single outbreaks for empirically plausible ranges of parameters, ignoring that a substantial part of these contacts would have been identified by manual contact tracing. This result is generally robust against changes in network topology with exceptions for homogeneous-degree, locally-clustered contact networks, on which the intervention prevents more infections. An improvement of efficacy is similarly observed when app participation is highly clustered. We find that DCT typically averts more cases during the super-critical phase of an epidemic when case counts are rising and the measured efficacy therefore depends on the time of evaluation. Author summary Many countries relied on non-pharmaceutical interventions (NPIs) to mitigate or contain outbreaks during the COVID-19 pandemic. Because NPIs can have a dramatic socio-economic impact, policy makers were looking for less severe interventions to combat this global crisis. Since mid-2020, digital contact tracing (DCT) solutions have been implemented in many countries, raising the expectation that this intervention may effectively contain outbreaks without requiring more severe NPIs. Analyzing a stochastic infectious-disease network model that captures the essential elements of realistic contact structures, disease dynamics, NPIs, testing, and is based on empirical results regarding app adoption and usage behavior, we estimate that the intervention’s success with regard to the expected reduction of infections is on the order of single-digit percentages, mostly regardless of contact network structure. Only when contact networks either exhibit both high local clustering and a narrow degree distribution, or when app participation is highly clustered will DCT efficacy be increased.

Project description:Objective Contact tracing is one of the key public health response actions to control the outbreak of a novel virus. This paper describes the preparation process, activation and operational experience for contact tracing of individuals in response to confirmed COVID-19 cases in Wales. Study design A descriptive approach has been adopted and lessons learned from our initial public health response to COVID-19 will be used to develop a new operational model for contact tracing in Wales. Methods As part of preparations for the response in Wales, Public Health Wales formed a Contact Tracing Cell (CTC) ready to be mobilised in the event of a confirmed case. Results Trial activation of the CTC during the preparation period helped to resolve some issues before ‘real’ activation. A highly flexible approach was needed due to the constant changes to the guidance that required rapid understanding, updates to pathways and clear communication to contact tracers. Conclusions Our experience and recommendations may benefit future efforts to control the spread of the virus in Wales and elsewhere, particularly in supporting COVID-19 outbreaks in enclosed settings such as care homes or in geographically localised areas. Learning from the initial public health response to COVID-19 will guide the delivery and implementation of a new contact tracing model as we move to a later stage of the pandemic when containment measures become feasible in localised outbreaks. This may include scaling-up the CTC to mobilise contact tracers to local teams and the potential use of digital technologies to support the next operational model of the CTC in Wales. Highlights • Contact tracing is one of the key public health response actions to control the outbreak of a novel virus.• For a CTC to be effective, identification and training of adequate numbers of staff needs to be done as early as possible.• A highly flexible approach was needed due to the logistical complexities of dealing with constant changes to the guidelines.• The mobilisation of non-health protection staff freed up health protection teams to deal with more complex issues.• Learning is needed to enhance future contact tracing activities when containment becomes feasible in localised outbreaks.

Dataset Information

The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19.

Publications

The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19.

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