Evidence map›Paper›PMID 38572005›Full record

ArticleFrontiers in public health2024

PANDEM-Source, a tool to collect or generate surveillance indicators for pandemic management: a use case with COVID-19 data.

Francisco Orchard, Charline Clain, William Madie, Jessica S Hayes, Máire A Connolly, Etienne Sevin, Alexis Sentís

Abstract read
In one paragraph

Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Francisco OrchardEpiconcept, Paris, France.
Charline ClainEpiconcept, Paris, France.
William MadieEpiconcept, Paris, France.
Jessica S HayesSchool of Medicine, University of Galway, Galway, Ireland.
Máire A ConnollySchool of Health Sciences, University of Galway, Galway, Ireland.
Etienne SevinEpiconcept, Paris, France.
Alexis SentísEpiconcept, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: PANDEM-Source (PS) is a tool to collect and integrate openly available public health-related data from heterogeneous data sources to support the surveillance of infectious diseases for pandemic management. The tool may also be used for pandemic preparedness by generating surveillance data for training purposes. It was developed as part of the EU-funded Horizon 2020 PANDEM-2 project during the COVID-19 pandemic as a result of close collaboration in a consortium of 19 partners, including six European public health agencies, one hospital, and three first responder organizations. This manuscript describes PS's features and design to disseminate its characteristics and capabilities to strengthen pandemic preparedness and response. Methods: A requirement-gathering process with EU pandemic managers in the consortium was performed to identify and prioritize a list of variables and indicators useful for surveillance and pandemic management. Using the COVID-19 pandemic as a use case, we developed PS with the purpose of feeding all necessary data to be displayed in the PANDEM-2 dashboard. Results: PS routinely monitors, collects, and standardizes data from open or restricted heterogeneous data sources (users can upload their own data). It supports indicators and health resources related data from traditional data sources reported by national and international agencies, and indicators from non-traditional data sources such as those captured in social and mass media, participatory surveillance, and seroprevalence studies. The tool can also calculate indicators and be used to produce data for training purposes by generating synthetic data from a minimal set of indicators to simulate pandemic scenarios. PS is currently set up for COVID-19 surveillance at the European level but can be adapted to other diseases or threats and regions. Conclusion: With the lessons learnt during the COVID-19 pandemic, it is important to keep building capacity to monitor potential threats and develop tools that can facilitate training in all the necessary aspects to manage future pandemics. PS is open source and its design provides flexibility to collect heterogeneous data from open data sources or to upload end users's own data and customize surveillance indicators. PS is easily adaptable to future threats or different training scenarios. All these features make PS a unique and valuable tool for pandemic management.

Indexed as

COVID-19HumansPandemicsPublic HealthSeroepidemiologic StudiesCOVID-19data collectiondata generationopen datapandemic managementpandemic preparednesspublic healthsurveillance

Identifiers

PMID38572005
PMCPMC10989069

What OpenQuestion holds

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Registered trials

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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.