Evidence map›Paper›PMID 34574847›Full record

ArticleInternational journal of environmental research and public health2021

Application of Pandemic Intelligence in Dynamic Data in Taiwan.

Tzu-Yin Chang, Wen-Ray Su, Hongey Chen, Ming-Wey Huang, Lu-Yen A Chen

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Tzu-Yin ChangNational Science and Technology Center for Disaster Reduction, New Taipei 23143, Taiwan.
Wen-Ray SuNational Science and Technology Center for Disaster Reduction, New Taipei 23143, Taiwan.
Hongey ChenNational Science and Technology Center for Disaster Reduction, New Taipei 23143, Taiwan.
Ming-Wey HuangNational Science and Technology Center for Disaster Reduction, New Taipei 23143, Taiwan.
Lu-Yen A ChenInstitute of Clinical Nursing, School of Nursing, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Taiwan was successful in containing the spread of the novel coronavirus (COVID-19) in 2020. One major factor in this success was the compilation and provision of comprehensive information about the pandemic. The present study proposes a pandemic intelligence system that provides data on the number of epidemic prevention professionals in each county and city, as well as daily confirmed cases, the demographics of the confirmed cases, and available resources (negative-pressure room beds and artificial ventilation apparatuses) in hospitals. Furthermore, the system provides the location of pharmacies selling masks and their current inventories, as well as the distribution of crowds at popular tourist destinations and social-distance monitoring. The most frequently used map layer in the thematic map of the pandemic is that of crowd distribution during the study period from March 2020 until the end of the same year. The case study used in this investigation for applying the system is represented by the 4-day weekend for Tomb-Sweeping Day of 2020. Through the real-time analysis of dynamic data and the integration of intelligence, the system offers a clear insight into changes in relevant information and, thus, enables the preemptive deployment of control measures by the county/city governments regarding pandemic management.

Indexed as

COVID-19PandemicsHumansIntelligenceSARS-CoV-2TaiwanCOVID-19data integrationdynamic intelligencepandemic management

Identifiers

PMID34574847
PMCPMC8467998

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Registered trials

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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.