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

OBDETECTOR 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.

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Publication Details

Journal
BMC Research Notes
Published
2024-08-20
DOI
https://doi.org/10.1186/s13104-024-06892-8
Citations
2
Primary Topic
Data-Driven Disease Surveillance
Type
article
Field-Weighted Citation Impact
0.77

Funders

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article

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

Bushra Zareie, Amin Roshani, Jalal Poorolajal, Manoochehr Karami et al.
2 citations
BMC Research Notes
Data-Driven Disease Surveillance
0.77
article

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

Bushra Zareie, Amin Roshani, Jalal Poorolajal, Manoochehr Karami, Ahmed Menbari
article en
2 citations

Abstract

OBDETECTOR 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.

BMC Research NotesVol. 17(1)
Hamedan University of Medical Sciences (IR), Iran University of Medical Sciences (IR), Islamic Azad University Sanandaj Branch (IR), Lorestan University (IR), Shahid Beheshti University of Medical Sciences (IR)
Hamadan University of Medical Sciences, Vice Chancellor for Research and Technology, Hamadan University of Medical Sciences
Good health and well-being
Openalex Percentile: Top 26%
Data-Driven Disease Surveillance
0.77
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