Evidence map›Paper›PMID 33303453›Full record

ArticleBMJ open2020

Early detection of COVID-19 in China and the USA: summary of the implementation of a digital decision-support and disease surveillance tool.

Yulin Hswen, John S Brownstein, Xiang Xu, Elad Yom-Tov

Abstract readComparative Study
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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  5. Article
  6. Review
  7. Observational
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

4 authors.

Yulin HswenEpidemiology and Biostatistics, Bakar Computational Health Institute, University of California San Francisco, San Francisco, California, USA yulin.hswen@ucsf.edu.ORCID http://orcid.org/0000-0003-3203-1322
John S BrownsteinComputational Epidemiology Lab, Harvard Medical School, Boston, Massachusetts, USA.
Xiang XuDepartment of Statistics, Boston University, Boston, Massachusetts, USA.
Elad Yom-TovMicrosoft Research, Herzeliya, Israel.ORCID http://orcid.org/0000-0002-2380-4584

Funding

An Approach for Estimating Foodborne Illnesses and Assessing Risk FactorsR01LM011965 · NLM · BOSTON CHILDREN'S HOSPITAL · PI BROWNSTEIN, JOHN S., NSOESIE, ELAINE O. · 2015 to 2018
$1.4M
NLM NIH HHS R01 LM011965
6 · The paper itself

Abstract

objectivesRapid detection and surveillance of COVID-19 is essential to reducing spread of the virus. Inadequate screening capacity has hampered COVID-19 detection, while traditional infectious disease response has been delayed due to significant demands for healthcare resources, time and personnel. This study investigated whether an online health decision-support tool could supplement COVID-19 surveillance and detection in China and the USA.

settingDaily website traffic to Thermia was collected from China and the USA, and cross-correlation analyses were used to assess the designated lag time between the daily time series of Thermia sessions and COVID-19 case counts from 22 January to 23 April 2020.

participantsThermia is a validated health decision-support tool that was modified to include content aimed at educating users about Centers for Disease Control and Prevention recommendations on COVID-19 symptoms. An advertising campaign was released on Microsoft Advertising to refer searches for COVID-19 symptoms to Thermia.

resultsThe lead times observed for Thermia sessions to COVID-19 case reports was 3 days in China and 19 days in the USA. We found negative cross-correlation between the number of Thermia sessions and rates of influenza A and B, possibly due to the decreasing prevalence of influenza and the lack of specificity of the system for identification of COVID-19.

conclusionThis study suggests that early deployment of an online campaign and modified health decision-support tool may support identification of emerging infectious diseases like COVID-19. Researchers and public health officials should deploy web campaigns as early as possible in an epidemic to detect, identify and engage those potentially at risk to help prevent transmission of the disease.

Indexed as

Decision Support Systems, ClinicalHealth PromotionInternetAdvertisingChinaCOVID-19Early DiagnosisHumansPopulation SurveillanceUnited Statesepidemiologyhealth informaticspublic health

Identifiers

PMID33303453
PMCPMC7733221

What OpenQuestion holds

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LicenceCC BY-NC
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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.