Evidence map›Paper›PMID 36812624›Full record

ArticlePLOS digital health2022

Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada's second COVID-19 wave.

Nathan Duarte, Rahul K Arora, Graham Bennett, Meng Wang, Michael P Snyder, Jeremy R Cooperstock, Caroline E Wagner

Open access · goldAbstract read
In one paragraph

Article in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 3 institutions in 3 countries.

Nathan DuarteDepartment of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada.ORCID https://orcid.org/0000-0003-1122-6067
Rahul K AroraDepartment of Community Health Sciences, University of Calgary, Calgary, Canada.
Graham BennettDepartment of Economics, Faculty of Arts, McGill University, Montreal, Canada.
Meng WangDepartment of Genetics, Stanford University School of Medicine, Stanford University, California, United States of America.
Michael P SnyderDepartment of Genetics, Stanford University School of Medicine, Stanford University, California, United States of America.
Jeremy R CooperstockDepartment of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada.
Caroline E WagnerDepartment of Bioengineering, Faculty of Engineering, McGill University, Montreal, Canada.
McGill University · CAStanford University · USUniversity of Calgary · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID36812624
PMCPMC9931244
OpenAlexW4294844176

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.