Evidence map›Paper›PMID 39164780›Full record

ArticleBMC research notes2024

Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics.

Bushra Zareie, Jalal Poorolajal, Amin Roshani, Ahmed Menbari, Manoochehr Karami

Abstract read
In one paragraph

Article in BMC research notes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Bushra ZareieDepartment of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Jalal PoorolajalDepartment of Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
Amin RoshaniDepartment of Statistics, Lorestan University, Khorramabad, Iran.
Ahmed MenbariDepartment of Computer Engineering, Sannandaj Branch, Islamic Azad University, Sanandaj, Iran.
Manoochehr KaramiDepartment of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. man.karami@yahoo.com.

Funding

Vice Chancellor for Research and Technology, Hamadan University of Medical Sciences No. 140009237703
6 · The paper itself

Abstract

objectiveDigital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks.

resultsOBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

Indexed as

AlgorithmsDisease OutbreaksInternetPopulation SurveillanceCommunicable DiseasesEpidemicsHumansSoftwareAutomatic outbreak detectionEarly warning systemsOutbreaksSyndromic surveillance systemsWeb application

Identifiers

PMID39164780
PMCPMC11334589

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.