Anatomy of digital contact tracing: Role of age, transmission setting, adoption, and case detection
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Abstract
Digital contact tracing apps could slow down COVID-19 transmission at moderate adoption: A model-based study.
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SciScore for 10.1101/2020.07.22.20158352: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study is affected by limitations. First, we analyzed the effect of digital contact tracing on COVID-19 incidence on the general population. Crucial information for public health authorities would be to quantify the effect in time of these measures on hospitalizations. This would require to couple our model for COVID-19 transmission …
SciScore for 10.1101/2020.07.22.20158352: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study is affected by limitations. First, we analyzed the effect of digital contact tracing on COVID-19 incidence on the general population. Crucial information for public health authorities would be to quantify the effect in time of these measures on hospitalizations. This would require to couple our model for COVID-19 transmission in the general population with a model describing disease severity and within-hospital patient trajectories [14], [17]. Second, the model does not account for transmission in nursing homes. This setting is where the majority of transmissions among elderly occurred. At the same time, however, the response to the COVID-19 epidemic in this setting relies mostly on routine screening of symptoms and frequent testing of residents, together with face masks and strict hygiene for visitors. Third, clustering effects are partially captured by the model thanks to the repetition of contacts, but effects may be larger in real contact patterns. Results obtained on real contact data, however, are similar to ours obtained on synthetically reconstructed contacts [15]. Eventually, parameterized with data from a social contact survey [25], the model cannot account for crowding events. These events were suggested to play an important role in the epidemic dynamics [12], and may also impact the effectiveness of contact tracing. Contact tracing may be more effective in networks showing large fluctuations in the number of contacts per individual [40]. Therefore, resul...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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