ArticleIEEE access : practical innovations, open solutions2018
DOVE: An Infectious Disease Outbreak Statistics Visualization System.
Article in IEEE access : practical innovations, open solutions, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Article
- Potential RNA-dependent RNA polymerase (RdRp) inhibitors as prospective drug candidates for SARS-CoV-2.European journal of medicinal chemistry · 2023Review
- Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.Sustainable cities and society · 2020Article
- PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System.IEEE access : practical innovations, open solutions · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Humans are susceptible to various infectious diseases. However, humanity still has limited responses to emergent and recurrent infectious diseases. Recent developments in medical technology have led to various vaccines being developed, but these vaccines typically require a considerable amount of time to counter infectious diseases. Therefore, one of the best methods to prevent infectious diseases is to continuously update our knowledge with useful information from infectious disease information systems and taking active steps to safeguard ourselves against infectious diseases. Some existing infectious disease information systems simply present infectious disease information in the form of text or transmit it via e-mail. Other systems provide data in the form of files or maps. Most existing systems display text-centric information regarding infectious disease outbreaks. Therefore, understanding infectious disease outbreak information at a glance is difficult for users. In this paper, we propose the infectious disease outbreak statistics visualization system, called to DOVE, which collects infectious disease outbreak statistics from the Korea Centers for Disease Control & Prevention and provides statistical charts with district, time, infectious disease, gender, and age data. Users can easily identify infectious disease outbreak statistics at a glance by simply entering the district, time, and name of an infectious disease into our system. Additionally, each statistical chart allows users to recognize the characteristics of an infectious disease and predict outbreaks by investigating the outbreak trends of that disease. We believe that our system provides effective information to help prevent infectious disease outbreaks. Our system is currently available on the web at http://www.epidemic.co.kr/statistics.
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Registered trials
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.