Modelling the impact of contact tracing of symptomatic individuals on the COVID-19 epidemic

Amaku, Marcos; Covas, Dimas Tadeu; Coutinho, Francisco Antônio Bezerra; Azevedo, Raymundo Soares; Massad, Eduardo

doi:10.6061/clinics/2021/e2639

Cited by 3 publications

(1 citation statement)

References 9 publications

(14 reference statements)

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“…Contact tracing is an effective tool to control transmission of infection 21 22 23 . Modelling studies have estimated the benefit from contact tracing and self-isolation to reduce SARS-CoV-2 transmission 24 25 26 . There is more limited evidence on the actual impact of the very large scale contact tracing undertaken during the pandemic.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases

Findlater

Pierotti

Turner³

et al. 2022

Preprint

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ObjectiveIn September 2020, records of 15,861 SARS-CoV-2 cases failed to upload from the Second Generation Laboratory Surveillance System (SGSS) to the Contact Tracing Advisory Service (CTAS) tool, resulting in a delay in the contact tracing of these cases. This study used CTAS data to determine the impact of this delay on health outcomes: transmission events, hospitalisations, and mortality. Previously, a modelling study had suggested a substantial impact.DesignObservational studySettingEngland.PopulationIndividuals testing positive for SARS-CoV-2 and their reported contacts.Main outcome measuresSecondary attack rates (SARs), hospitalisations, and deaths amongst primary and secondary contacts were calculated, compared to all other concurrent, unaffected cases. SGSS records affected by the event were matched to CTAS records and successive contacts and cases were identified.ResultsThe initiation of contact tracing was delayed by 3 days on average in the primary cases in the delay group (6 days) compared to the control group (3 days). This was associated with lower completion of contact tracing of primary cases in the delay group: 80% (95%CI: 79-81%) in the delay group and 83% (95%CI: 83-84%) in the control group. There was some evidence to suggest an increase in transmission to non-household contacts amongst those affected by the delay. The SAR for non-household contacts was higher amongst secondary contacts in the delay group than the control group (delay group: 7.9%, 95%CI:6.4% to 9.2%; control group: 5.9%, 95%CI: 5.3% to 6.6%). There was no evidence of a difference between the delay and control groups in the odds of hospitalisation (crude odds ratio: 1.1 (95%CI: 0.9 to 1.2) or death (crude odds ratio: 0.7 (0.1 to 4.0)) amongst secondary contacts.ConclusionsThe delay in contact tracing had a limited impact on population health outcomes.Strengths and limitations of the studyShows empirical data on the health impact of an event leading to a delay in contact tracing so can test hypotheses generated by models of the potential impact of a delay in contact tracingEstimates the extent of further transmission and odds of increased mortality or hospitalisation in up to the third generation of cases affected by the eventThe event acts as a natural experiment to describe the possible impact of contact tracing, comparing a group affected by chance by delayed contact tracing to a control group who experienced no delayContact tracing was not completed for all individuals, so the study might not capture all affected contacts or transmissions

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Section: Discussionmentioning

confidence: 99%

Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases

Findlater

Pierotti

Turner³

et al. 2022

Preprint

View full text Add to dashboard Cite

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Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases in England, September 2020: an observational study

Findlater,

Pierotti,

Turner

et al. 2023

BMJ Open

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ObjectiveIn September 2020, 15 861 SARS-CoV-2 case records failed to upload from the Second Generation Surveillance System (SGSS) to the Contact Tracing Advisory Service (CTAS) tool, delaying the contact tracing of these cases. This study used CTAS data to determine the impact of this delay on population health outcomes: transmission events, hospitalisations and mortality. Previously, a modelling study suggested a substantial impact.DesignObservational study.SettingEngland.PopulationIndividuals testing positive for SARS-CoV-2 and their reported contacts.Main outcome measuresSecondary attack rates (SARs), hospitalisations and deaths among primary and secondary contacts were calculated, compared with all other concurrent, unaffected cases. Affected SGSS records were matched to CTAS records. Successive contacts and cases were identified and matched to hospital episode and mortality outcomes.ResultsInitiation of contact tracing was delayed by 3 days on average in the primary cases in the delay group (6 days) compared with the control group (3 days). This was associated with lower completion of contact tracing: 80% (95% CI: 79% to 81%) in delay group and 83% (95% CI: 83% to 84%) in control group. There was some evidence to suggest increased transmission to non-household contacts among those affected by the delay. The SAR for non-household contacts was higher among secondary contacts in the delay group than the control group (delay group: 7.9%, 95% CI: 6.5% to 9.2%; control group: 5.9%, 95% CI: 5.3% to 6.6%). There did not appear to be a significant difference between the delay and control groups in the odds of hospitalisation (crude OR: 1.1 (95% CI: 0.9 to 1.2)) or death (crude OR: 0.7 (95% CI: 0.1 to 4.0)) among secondary contacts.ConclusionsOur analysis suggests that the delay in contact tracing had a limited impact on population health outcomes; however, contact tracing was not completed for all individuals, so some transmission events might not be captured.

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Compartmental structures used in modeling COVID-19: a scoping review

et al. 2022

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Background The coronavirus disease 2019 (COVID-19) epidemic, considered as the worst global public health event in nearly a century, has severely affected more than 200 countries and regions around the world. To effectively prevent and control the epidemic, researchers have widely employed dynamic models to predict and simulate the epidemic’s development, understand the spread rule, evaluate the effects of intervention measures, inform vaccination strategies, and assist in the formulation of prevention and control measures. In this review, we aimed to sort out the compartmental structures used in COVID-19 dynamic models and provide reference for the dynamic modeling for COVID-19 and other infectious diseases in the future. Main text A scoping review on the compartmental structures used in modeling COVID-19 was conducted. In this scoping review, 241 research articles published before May 14, 2021 were analyzed to better understand the model types and compartmental structures used in modeling COVID-19. Three types of dynamics models were analyzed: compartment models expanded based on susceptible-exposed-infected-recovered (SEIR) model, meta-population models, and agent-based models. The expanded compartments based on SEIR model are mainly according to the COVID-19 transmission characteristics, public health interventions, and age structure. The meta-population models and the agent-based models, as a trade-off for more complex model structures, basic susceptible-exposed-infected-recovered or simply expanded compartmental structures were generally adopted. Conclusion There has been a great deal of models to understand the spread of COVID-19, and to help prevention and control strategies. Researchers build compartments according to actual situation, research objectives and complexity of models used. As the COVID-19 epidemic remains uncertain and poses a major challenge to humans, researchers still need dynamic models as the main tool to predict dynamics, evaluate intervention effects, and provide scientific evidence for the development of prevention and control strategies. The compartmental structures reviewed in this study provide guidance for future modeling for COVID-19, and also offer recommendations for the dynamic modeling of other infectious diseases. Graphical Abstract "Image missing"

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Modelling the impact of contact tracing of symptomatic individuals on the COVID-19 epidemic

Cited by 3 publications

References 9 publications

Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases

Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases

Evaluating the impact on health outcomes of an event that resulted in a delay in contact tracing of COVID-19 cases in England, September 2020: an observational study

Compartmental structures used in modeling COVID-19: a scoping review

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