Evidence map›Paper›PMID 41346739›Full record

ArticleFrontiers in public health2025

Evaluating the performance of digital surveillance for epidemic-prone diseases in Kwango Province, Democratic Republic of Congo.

Ayuko Hirai, Bernard Silenou, Tommy Bukasa, Baweye M Barka, François Mwakisenda, Juliane Dörrbecker, Tshotsho Dixon, Aruna Aaron, Mathias Altmann

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. 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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
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

9 authors.

Ayuko HiraiNational Institute for Health and Medical Research (Inserm) UMR 1219, Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux Population Health Research Centre, University of Bordeaux, Bordeaux, France.
Bernard SilenouHelmholtz Centre for Infection Research, Braunschweig, Germany.
Tommy BukasaThe Alliance for International Medical Action (ALIMA), Kinshasa, Democratic Republic of Congo.
Baweye M BarkaThe Alliance for International Medical Action (ALIMA), Kinshasa, Democratic Republic of Congo.
François MwakisendaProvincial Health Division, Kwango, Democratic Republic of Congo.
Juliane DörrbeckerHelmholtz Centre for Infection Research, Braunschweig, Germany.
Tshotsho DixonProvincial Health Division, Kwango, Democratic Republic of Congo.
Aruna AaronMinistry of Health, Kinshasa, Democratic Republic of Congo.
Mathias AltmannNational Institute for Health and Medical Research (Inserm) UMR 1219, Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux Population Health Research Centre, University of Bordeaux, Bordeaux, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Strengthening infectious disease surveillance systems is critical to prevent the spread of diseases, particularly in resource-limited settings. Digital health tools such as the Surveillance Outbreak Response Management and Analysis System (SORMAS) offer real-time reporting and data management. This study assessed the performance of SORMAS in Kwango Province, Democratic Republic of Congo (DRC), focusing on two implementation models: facility-level model, where health workers at health facilities entered data directly into SORMAS, and health zone-level model, where data entry was centralized at the health zone office. Methods: From July 2022 to December 2024, SORMAS was piloted for case-based reporting of epidemic-prone diseases in the Kenge Health Zone via the facility-level model and in 13 other health zones via the health zone-level model. We evaluated the completeness and timeliness of case-based reporting, as well as concordance with the conventional paper-based weekly epidemiological reports (WERs). SORMAS user characteristics were obtained through a telephone survey. Results: A total of 2,950 cases were registered in SORMAS between July 2022 and August 2024. The completeness of non-mandatory epidemiological data exceeded 80% across both implementation models. Timely reporting (within 1 day) was significantly greater under the facility-level model (46.0%) than under the health zone-level model (5.0%). SORMAS reported more cases than WERs under facility-level implementation, whereas WERs captured more cases than SORMAS under the health zone-level model. Conclusion: SORMAS is a viable tool for enhancing disease surveillance in the DRC, particularly when implemented at the health facility level. This pilot demonstrates the potential of digital tools to improve outbreak preparedness and response in resource-limited settings.

Indexed as

Disease OutbreaksEpidemicsPopulation SurveillanceDemocratic Republic of the CongoHumansDemocratic Republic of Congodigital health technologyeHealthepidemic preparednessinfectious disease surveillanceSORMAS

Identifiers

PMID41346739
PMCPMC12672494

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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.