Evidence map›Paper›PMID 40082077›Full record

ArticleEpidemiology and infection2025

Data for action - description of the automated COVID-19 surveillance system in Denmark and lessons learnt, January 2020 to June 2024.

Gudrun Witteveen-Freidl, Karina Lauenborg Møller, Marianne Voldstedlund, Sophie Gubbels, Statens Serum Institut COVID-19 Automated Surveillance Group

Erratum issuedAbstract read
In one paragraph

Article in Epidemiology and infection, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  5. From rare to recognized: enhanced detection uncoversEmerging microbes & infections · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Gudrun Witteveen-FreidlDepartment of Data Integration and Analysis, Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark.ORCID https://orcid.org/0000-0003-0702-0189
Karina Lauenborg MøllerDepartment of Data Integration and Analysis, Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark.
Marianne VoldstedlundDepartment of Data Integration and Analysis, Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark.
Sophie GubbelsDepartment of Data Integration and Analysis, Infectious Disease Preparedness, Statens Serum Institut, Copenhagen, Denmark.
Statens Serum Institut COVID-19 Automated Surveillance Group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Denmark is one of the leading countries in establishing digital solutions in the health sector. When SARS-CoV-2 arrived in February 2020, a real-time surveillance system could be rapidly built on existing infrastructure, This rapid data integration for COVID-19 surveillance enabled a data-driven response. Here we describe (a) the setup of the automated, real-time surveillance and vaccination monitoring system for COVID-19 in Denmark, including primary stakeholders, data sources, and algorithms, (b) describe outputs for various stakeholders, (c) how outputs were used for action and (d) reflect on challenges and lessons learnt. Outputs were tailored to four main stakeholder groups: four outputs provided direct information to individual citizens, four to complementary systems and researchers, 25 to decision-makers, and 15 informed the public, aiding transparency. Core elements in infrastructure needed for automated surveillance had been in place for more than a decade. The COVID-19 epidemic was a pressure test that allowed us to explore the system's potential and identify challenges for future pandemic preparedness. The system described here constitutes a model for the future infectious disease surveillance in Denmark. With the current pandemic threat posed by avian influenza viruses, lessons learnt from the COVID-19 pandemic remain topical and relevant.

Indexed as

COVID-19Epidemiological MonitoringPopulation SurveillanceDenmarkHumansPandemicsSARS-CoV-2AutomationCOVID-19DenmarkPublic Health SurveillanceSARS-CoV-2Systems Integration

Identifiers

PMID40082077
PMCPMC12001143

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.