Evidence map›Paper›PMID 32654885›Full record

ArticleBiomedical journal2020

Development of a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation: e-Outbreak Platform.

Wei J Chen, Shih-Yi Yang, Jung-Chih Chang, Wei-Che Cheng, Tzu-Pin Lu, Yu-Neng Wang, Ming-Hao Juan, Ruey-Tzer Hsu, Song-Ren Huang, Jia-Jang Tu and 3 more

Abstract read
In one paragraph

Article in Biomedical journal, 2020. 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
–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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  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

13 authors.

Wei J ChenCenter for Neuropsychiatric Research, National Health Research Institutes, Zhunan, Miaoli, Taiwan; Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan. Electronic address: wjchen@nhri.edu.tw.
Shih-Yi YangPublic Sector, Microsoft Taiwan, Taipei, Taiwan.
Jung-Chih ChangSolutions Technical Unit, Microsoft Taiwan, Taipei, Taiwan.
Wei-Che ChengPublic Sector, Microsoft Taiwan, Taipei, Taiwan.
Tzu-Pin LuInstitute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
Yu-Neng WangDepartment of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Ming-Hao JuanDepartment of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Ruey-Tzer HsuDepartment of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
Song-Ren HuangDepartment of Computer Science & Information Engineering, National Taiwan University, Taipei, Taiwan.
Jia-Jang TuComputational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan.
Pang-Chieh WangComputational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan.
Vincent W-S FengComputational Intelligence Technology Center, Industrial Technology Research Institute, Hsinchu, Taiwan.
Pei-Zen ChangIndustrial Technology Research Institute, Hsinchu, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aggressive tracing of contacts of confirmed cases is crucial to Taiwan's successful control of the early spread of COVID-19. As the pandemic lingers, an epidemiological investigation that can be conducted efficiently in a timely manner can help decrease the burden on the health personnel and increase the usefulness of such information in decision making. To develop a new tool that can improve the current practice of epidemiological investigation by incorporating new technologies in digital platform and knowledge graphs. To meet the various needs of the epidemiological investigation, we decided to develop an e-Outbreak Platform that provides a semi-structured, multifaceted, computer-aided questionnaire for outbreak investigation. There are three major parts of the platform: (1) a graphic portal that allows users to have an at-glance grasp of the functions provided by the platform and then choose the one they need; (2) disease-specific questionnaires that can accommodate different formats of the information, including text typing, button selection, and pull-down menu; and (3) functions to utilize the stored information, including report generation, statistical analyses, and knowledge graphs displaying contact tracing. When the number of outbreak investigation increases, the knowledge graphs can be extended to encompass other persons appearing in the same location at the same time, i.e., constituting a potential contact cluster. The information extracted can also be used to display the tracing on a map in animation. Overall, this system can provide a basis for further refinement that can be generalized to a variety of outbreak investigations.

Indexed as

Surveys and QuestionnairesBetacoronavirusCoronavirus InfectionsCOVID-19Disease OutbreaksFemaleHumansMalePandemicsPneumonia, ViralSARS-CoV-2TaiwanCOVID-19Epidemiological investigationKnowledge graphOutbreakPlatformSARS-CoV-2

Identifiers

PMID32654885
PMCPMC7305507

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.