Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada’s second COVID-19 wave
This article has been Reviewed by the following groups
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
- Evaluated articles (ScreenIT)
Abstract
Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada’s second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.
Article activity feed
-
-
SciScore for 10.1101/2022.02.07.22270634: (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 work has important limitations. First, we assumed that SARS-CoV-2 epidemiology and wearable device use were homogenous within the population. Our determination of the transmission rate from epidemiological data inherently results in a “well-mixed” approximation for this value averaged over population-level heterogeneities such as age …
SciScore for 10.1101/2022.02.07.22270634: (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 work has important limitations. First, we assumed that SARS-CoV-2 epidemiology and wearable device use were homogenous within the population. Our determination of the transmission rate from epidemiological data inherently results in a “well-mixed” approximation for this value averaged over population-level heterogeneities such as age and super-spreading. As well, the COVID-19 pandemic has disproportionately impacted low-income and minority groups, while younger and wealthier individuals are more likely to own wearable devices25,26. Future studies could consider a policy where the incentive to download the application is increased to become a subsidy for purchasing a wearable device, reducing the participation barrier. Second, we used an existing model for the incidence of infection which has its own assumptions and limitations13. Third, we made the simplifying assumption that all users without symptoms (and that no users with symptoms) could benefit from wearable-informed prompts to take a confirmatory test and self-isolate. Fourth, we did not consider how uptake or adherence might vary over time or with detection accuracy18,22. Fifth, we used median values for SARS-CoV-2 infection parameters (e.g., latent period) and did not account for reinfections or vaccinations. Finally, we did not account for administrative costs or second order savings (e.g., avoided worker’s compensation payouts for 14-day quarantines). To our knowledge, this is the first study to explore the real...
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.
Results from scite Reference Check: We found no unreliable references.
-
