Evidence map›Paper›PMID 42743586›Full record

ArticleJMIR public health and surveillance2026

Requirements and Implementation Challenges of Automated Outbreak Detection Systems in the European Union: Mixed Methods Study.

Paulina Vom Felde Genannt Imbusch, Ann Christin Vietor, Inessa Markus, Michaela Diercke, Alexander Ullrich

Abstract read
In one paragraph

Article in JMIR public health and surveillance, 2026. 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

5 authors.

Paulina Vom Felde Genannt ImbuschDepartment of Infectious Diseases Epidemiology, Robert Koch Institute, Seestr. 10, Berlin, State of Berlin, 13353, Germany, 49 30187540.ORCID http://orcid.org/0000-0002-0872-8990
Ann Christin VietorDepartment of Infectious Diseases Epidemiology, Robert Koch Institute, Seestr. 10, Berlin, State of Berlin, 13353, Germany, 49 30187540.ORCID http://orcid.org/0009-0008-5392-1774
Inessa MarkusDepartment of Infectious Diseases Epidemiology, Robert Koch Institute, Seestr. 10, Berlin, State of Berlin, 13353, Germany, 49 30187540.ORCID http://orcid.org/0009-0005-1663-3499
Michaela DierckeDepartment of Infectious Diseases Epidemiology, Robert Koch Institute, Seestr. 10, Berlin, State of Berlin, 13353, Germany, 49 30187540.ORCID http://orcid.org/0000-0002-4678-1813
Alexander UllrichDepartment of Infectious Diseases Epidemiology, Robert Koch Institute, Seestr. 10, Berlin, State of Berlin, 13353, Germany, 49 30187540.ORCID http://orcid.org/0000-0002-4894-6124

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Automated outbreak detection can enhance infectious disease surveillance by enabling early identification of outbreaks and supporting timely public health measures. However, information on its current use by national public health institutes remains limited. Objective: This study aimed to provide an updated and extended overview of automated outbreak detection use in the European Union and the United Kingdom by (1) assessing current demand, (2) examining the availability of key prerequisites within existing surveillance systems, and (3) identifying challenges and requirements for implementation. Methods: A mixed methods approach was applied as part of the Joint Action UNITED4Surveillance. Data were collected between April 2023 and January 2024 through an online survey sent to 25 countries, an in-person workshop with experts from 16 countries, and follow-up meetings with participating national public health institutes. Additional information for selected countries was obtained from the literature. Data were analyzed descriptively. Results: Twenty-one countries completed the survey. In total, 33.3% (n=7) of the countries had established automated outbreak detection systems, 19% (n=4) were planning implementation, and 47.6% (n=10) participated in a pilot project. All surveyed countries reported acting on detected surveillance signals. Most had suitable surveillance infrastructure, including case-based data (n=20, 95.2%), daily reporting (n=21, 100%), and at least 3 years of historical data (n=21, 100%). Main barriers to implementation included limited funding (n=15, 71.4%), insufficient IT capacity, and data quality issues (n=9, 42.9%). Despite heterogeneity in methods and system design, outputs and user requirements were largely similar across countries, with needs for flexible outputs, stratification, and user-friendly interfaces. Conclusions: While the specific methods in existing automated outbreak detection systems differ, overall demands and outputs are similar, suggesting that a single tool could serve multiple countries. European Union-funded Joint Actions may help address some of these barriers by supporting collaborative tool development and knowledge exchange.

Indexed as

AutomationDisease OutbreaksPopulation SurveillanceEuropean UnionHumansPublic Health InfrastructureSurveys and QuestionnairesUnited Kingdomalgorithmscommunicable diseasesdisease outbreakspublic health informaticspublic health surveillance

Identifiers

PMID42743586
PMCPMC13577613

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