Evidence map›Paper›PMID 32214691›Full record

ArticleJournal of intelligent manufacturing2014

The construction of a hospital disease tracking and control system with a disease infection probability model.

Yi Chao Huang, Qianyi Xie

Open access · bronzeAbstract read
In one paragraph

Article in Journal of intelligent manufacturing, 2014. 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
1.7field-weighted citation impact, top 12% 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

0 citing papers in PubMed, 6 citations in OpenAlex.

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

2 authors at 1 institution in 1 country.

Yi Chao HuangDepartment of Industrial Management, National Pingtung University of Science and Technology, 1 Hseuh-Fu Road, Nei-Pu Hsiang, Pingtung, 91201 Taiwan.
Qianyi XieDepartment of Industrial Management, National Pingtung University of Science and Technology, 1 Hseuh-Fu Road, Nei-Pu Hsiang, Pingtung, 91201 Taiwan.
National Pingtung University of Science and Technology · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With relatively short latency and rapid propagation, viral diseases could be transmitted through the air to medical personnel or the public during the incubation period. To reduce the possibilities of spread, this research creates an infection probability model based on the settling velocity and concentration distribution of infectious droplets. Then, radio frequency identification (RFID) technology is employed to track the travel history (time, date and place) of the infected patients. A tree structure algorithm and an infection probability model are applied to trace the transmission routes, discover the correlations between carriers and suspected cases, and finally calculate the infection probability on the basis of time interval. In case of an epidemic outbreak or once an infected case is confirmed, the disease tracking and control system could be initiated by accessing RFID logs to plot the carriers' time of onset and to trace possible routes of transmission via tree diagrams. The disease tracking and control system developed in this research can assist hospitals in assessing the risk of infection among medical personnel, as well as in prompt implementation of infection prevention and control measures, in order to reduce hospital acquired infections and provide a safe health care setting.

Indexed as

Disease tracking and control systemInfluenza ARadio frequency identification (RFID)Tree structure algorithm

Identifiers

PMID32214691
PMCPMC7087954
OpenAlexW2028736398

What OpenQuestion holds

Textmetadata
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.