Evidence map›Paper›PMID 38605877›Full record

ArticleFrontiers in public health2024

Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.

Forsad Al Hossain, M Tanjid Hasan Tonmoy, Sri Nuvvula, Brittany P Chapman, Rajesh K Gupta, Andrew A Lover, Rhoel R Dinglasan, Stephanie Carreiro, Tauhidur Rahman

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

9 authors.

Forsad Al HossainManning College of Information and Computer Sciences, University of Massachusetts-Amherst, Amherst, MA, United States.
M Tanjid Hasan TonmoyHalıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States.
Sri NuvvulaDepartment of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States.
Brittany P ChapmanDepartment of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States.
Rajesh K GuptaHalıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States.
Andrew A LoverSchool of Public Health & Health Sciences, University of Massachusetts Amherst, Amherst, MA, United States.
Rhoel R DinglasanInfectious Diseases and Immunology, University of Florida, Gainesville, FL, United States.
Stephanie CarreiroDepartment of Emergency Medicine, UMass Chan Medical School, Worcester, MA, United States.
Tauhidur RahmanHalıcıoǧlu Data Science Institute, University of California, San Diego, San Diego, CA, United States.

Funding

iTransform: Wearable Biosensors to Detect the Evolution of Opioid Tolerance in Opioid Naïve IndividualsK23DA045242 · NIDA · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI CARREIRO, STEPHANIE P · 2019 to 2021
$566k
NIDA NIH HHS K23 DA045242
6 · The paper itself

Abstract

Syndromic surveillance is an effective tool for enabling the timely detection of infectious disease outbreaks and facilitating the implementation of effective mitigation strategies by public health authorities. While various information sources are currently utilized to collect syndromic signal data for analysis, the aggregated measurement of cough, an important symptom for many illnesses, is not widely employed as a syndromic signal. With recent advancements in ubiquitous sensing technologies, it becomes feasible to continuously measure population-level cough incidence in a contactless, unobtrusive, and automated manner. In this work, we demonstrate the utility of monitoring aggregated cough count as a syndromic indicator to estimate COVID-19 cases. In our study, we deployed a sensor-based platform (

Indexed as

COVID-19Sentinel SurveillanceDisease OutbreaksFeverHospitalsHumansWaiting Roomsambient sensingcough countingemergency medicinerespiratory illnesssyndromic surveillance

Identifiers

PMID38605877
PMCPMC11007176

What OpenQuestion holds

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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.