Evidence map›Paper›PMID 39901070›Full record

ArticleBMC infectious diseases2025

Enhancing epidemic preparedness: a data-driven system for managing respiratory infections.

Moslem Sarani, Katayoun Jahangiri, Manoochehr Karami, Mohammadreza Honarvar

Abstract read
In one paragraph

Article in BMC infectious diseases, 2025. 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
–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

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

4 authors.

Moslem SaraniDepartment of Health in Disasters and Emergencies, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Katayoun JahangiriDepartment of Health in Disasters and Emergencies, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran. k.jahangiri@sbmu.ac.ir.ORCID http://orcid.org/0000-0003-2061-1418
Manoochehr KaramiDepartment of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammadreza HonarvarDepartment of Nutrition, Golestan University of Medical Sciences, Gorgan, Iran.

Funding

Shahid Beheshti University of Medical Sciences IR.SBMU.PHNS.REC.1402.003
6 · The paper itself

Abstract

backgroundEffective epidemic preparedness is critical for minimizing the health and societal impacts of viral respiratory infections. This study details the development of a data-driven early warning system (EWS) designed to improve outbreak detection and response utilizing the data integration and visualization capabilities of Microsoft Power BI.

methodsThis research utilized a structured three-phase approach to design a respiratory infections (RIs) management dashboard. Phase 1, focused on identifying critical variables through literature reviews and expert interviews. In Phase 2, Microsoft Power BI was employed for dashboard development, integrating data from diverse sources. Phase 3 involved usability testing with health professionals who evaluated navigation, data accuracy, decision-support features, providing feedback to enhance visualization clarity and filtering capabilities.

resultsKey data categories include individual-level variables, such as age, symptoms, and vaccination records, alongside population-level metrics like infection rates and regional vaccination coverage enabling functionalities such as identifying high-risk individuals, tracking infection dynamics, and optimizing resource allocation. The dashboard, developed using Power BI visualizes epidemiological trends, intervention outcomes, and resource utilization. A relational database schema ensures efficient data retrieval, facilitating comprehensive analysis.

conclusionThe prototype EWS represents a scalable and integrative framework aimed at enhancing public health applications, particularly in the context of respiratory infections. By incorporating data from diverse health sectors, the system offers decision-makers access to critical epidemiological indicators, supporting early outbreak detection and improved epidemic management. Its potential to unify health institutions underscores its value in fostering a more cohesive and effective approach to epidemic preparedness. Nevertheless, while the system demonstrates significant promise, further evaluation in real-world settings is essential to determine its practical impact on public health outcomes and its ability to mitigate health crises.

Indexed as

EpidemicsRespiratory Tract InfectionsDisease OutbreaksHumansPandemic PreparednessData-DrivenEarly warning system (EWS (Epidemic preparednessMicrosoft Power BIOutbreak detectionRespiratory infections (RIs (

Identifiers

PMID39901070
PMCPMC11792683

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.