Evidence map›Paper›PMID 36438983›Full record

ArticleFrontiers in big data2022

Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles.

Amit Klein, Karena Puldon, Stephan Dilchert, Wendy Hartogensis, Anoushka Chowdhary, Claudine Anglo, Leena S Pandya, Frederick M Hecht, Ashley E Mason, Benjamin L Smarr

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in big data, 2022. 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
0.6field-weighted citation impact, top 27% 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

1 citing paper in PubMed, 4 citations in OpenAlex.

  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

10 authors at 3 institutions in 1 country.

Amit KleinDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
Karena PuldonOsher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States.
Stephan DilchertDepartment of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY, United States.
Wendy HartogensisOsher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States.
Anoushka ChowdharyOsher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States.
Claudine AngloOsher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States.
Leena S PandyaOsher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States.
Frederick M HechtOsher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States.
Ashley E MasonOsher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States.
Benjamin L SmarrDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, United States.
University of California, San Francisco · USUniversity of California, San Diego · USBaruch College · US

Funding

Mentoring and Research in Integrative MedicineK24AT007827 · NCCIH · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HECHT, FREDERICK M · 2013 to 2022
$1.8M
NCCIH NIH HHS K24 AT007827
6 · The paper itself

Abstract

Background: Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. Objective: Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex. Methods: We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Results: Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females. Conclusion: Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.

Indexed as

Bayesian networkinfectious diseasemHealthpublic healthsex as a biological variable

Identifiers

PMID36438983
PMCPMC9685297
OpenAlexW4308872804

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.