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Review 1: "Contact Tracing Efficiency, Transmission Heterogeneity, and Accelerating COVID-19 Epidemics"

Published onMar 03, 2022
Review 1: "Contact Tracing Efficiency, Transmission Heterogeneity, and Accelerating COVID-19 Epidemics"
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key-enterThis Pub is a Review of
Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics
Description

AbstractSimultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19. However, manual contact tracing is a time-consuming process and as case numbers increase it takes longer to reach each cases’ contacts, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using a stochastic compartment model of SARS-CoV-2. We found that Rt increases sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections is more complicated). Contact tracing efficacy also decreased with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases, and the value of easy access, high testing capacity and rapid turn-around of testing results, as well as outreach efforts to encourage symptomatic infections to be tested immediately after symptom onset.Author SummaryA key tool in the control of infectious diseases is contact tracing – the identification of individuals who have contacted the case and may have been infected by a newly detected case. However, to successfully contact and quarantine individuals requires time, and as cases rise, this can result in delays in reaching contacts during which time they may infect other people. Here we examine the quantitative relationships between increasing case numbers, contact tracing efficiency, and the pathogen reproductive number Rt (the number of cases infected by each case) and how these relationships vary with delays and incomplete participation in the testing and tracing process. We built

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review:

This is a helpful modelling study taking a careful look at the impact of contact tracing on Covid-19 transmission dynamics, specifically the reproductive number. The authors have made use of an SEIR model that allows the researchers to explore various dimensions of contact tracing (e.g. timing, caseload per tracer, percent of infectious contacts traced). The model also incorporates social distancing measures. Overall this is a useful paper that appears to be well-done. The code is provided so others can modify to their particular scenarios. The model appears to be appropriately set up. A few notable limitations exist:

1. There are assumed exponential distributions in many places due to the nature of the model. This is not consistent with what is known about Covid-19 epidemiology. More sensitivity analyses would improve the robustness of the results.

2. The paper really highlights the need to have wide testing availability to make sure cases are identified and traced early. This could be emphasized more. In fact universities with strong testing and tracing protocols are demonstrating this.

3. More demonstration of the stochastic results would be helpful. There is clearly substantial stochastic variability in infectious disease dynamics and running the model more stochastically would be helpful.

4. A bit more detail on how calibration was done would be helpful. It is not clear exactly how that was done and f it is rigorous enough that the actual model outputs can be trusted.

5. Related to the previous comment, it is hard to know how much to trust the actual values produced from this exercise as opposed to relative values. Given the wide variability of communities and stochasticity of covid, relative values might be more meaningful.

6. It should be noted that this modelling strategy assumes homogenous mixing, which is most likely violated-it is not clear what impact this would have. It would be nice to see this analysis replicated in a network type model that can explore these heterogeneities further.


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