Evidence map›Paper›PMID 37490846›Full record

ArticleJMIR public health and surveillance2023

Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View.

Onicio Leal Neto, Daniela Paolotti, Craig Dalton, Sandra Carlson, Patipat Susumpow, Matt Parker, Polowat Phetra, Eric H Y Lau, Vittoria Colizza, Albert Jan van Hoek and 3 more

Open access · goldAbstract read
In one paragraph

Article in JMIR public health and surveillance, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
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

9 citing papers in PubMed, 13 citations in OpenAlex.

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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

13 authors at 7 institutions in 7 countries.

Onicio Leal NetoEnding Pandemics, San Francisco, CA, United States.ORCID 0000-0001-5785-1867
Daniela PaolottiInstitute for Scientific Interchange Foundation, Turin, Italy.ORCID 0000-0003-1356-3470
Craig DaltonFluTracking, Newcastle, Australia.ORCID 0000-0002-2005-7835
Sandra CarlsonFluTracking, Newcastle, Australia.ORCID 0000-0002-1400-4518
Patipat SusumpowOpendream, Bangkok, Thailand.ORCID 0000-0002-0178-886X
Matt ParkerOpendream, Bangkok, Thailand.ORCID 0009-0008-5204-8205
Polowat PhetraOpendream, Bangkok, Thailand.ORCID 0000-0002-4411-8970
Eric H Y LauSchool of Public Health, University of Hong Kong, Hong Kong, China.ORCID 0000-0002-6688-9637
Vittoria ColizzaPierre Louis Institute of Epidemiology and Public Health, INSERM, Sorbonne Université, Paris, France.ORCID 0000-0002-2113-2374
Albert Jan van HoekNational Institute for Public Health and the Environment, Bilthoven, Netherlands.ORCID 0000-0002-9244-0489
Charlotte KjelsøStatens Serum Institute, Copenhagen, Denmark.ORCID 0000-0002-9251-6025
John S BrownsteinBoston Children Hospital, Harvard University, Boston, MA, United States.ORCID 0000-0001-8568-5317
Mark S SmolinskiEnding Pandemics, San Francisco, CA, United States.ORCID 0000-0002-5713-8241
Boston Children's Hospital · USETH Zurich · CHInserm · FRInstitute for Scientific Interchange · ITNational Institute for Public Health and the Environment · NLStatens Serum Institut · DKUniversity of Hong Kong · HK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Participatory surveillance (PS) has been defined as the bidirectional process of transmitting and receiving data for action by directly engaging the target population. Often represented as self-reported symptoms directly from the public, PS can provide evidence of an emerging disease or concentration of symptoms in certain areas, potentially identifying signs of an early outbreak. The construction of sets of symptoms to represent various disease syndromes provides a mechanism for the early detection of multiple health threats. Global Flu View (GFV) is the first-ever system that merges influenza-like illness (ILI) data from more than 8 countries plus 1 region (Hong Kong) on 4 continents for global monitoring of this annual health threat. GFV provides a digital ecosystem for spatial and temporal visualization of syndromic aggregates compatible with ILI from the various systems currently participating in GFV in near real time, updated weekly. In 2018, the first prototype of a digital platform to combine data from several ILI PS programs was created. At that time, the priority was to have a digital environment that brought together different programs through an application program interface, providing a real time map of syndromic trends that could demonstrate where and when ILI was spreading in various regions of the globe. After 2 years running as an experimental model and incorporating feedback from partner programs, GFV was restructured to empower the community of public health practitioners, data scientists, and researchers by providing an open data channel among these contributors for sharing experiences across the network. GFV was redesigned to serve not only as a data hub but also as a dynamic knowledge network around participatory ILI surveillance by providing knowledge exchange among programs. Connectivity between existing PS systems enables a network of cooperation and collaboration with great potential for continuous public health impact. The exchange of knowledge within this network is not limited only to health professionals and researchers but also provides an opportunity for the general public to have an active voice in the collective construction of health settings. The focus on preparing the next generation of epidemiologists will be of great importance to scale innovative approaches like PS. GFV provides a useful example of the value of globally integrated PS data to help reduce the risks and damages of the next pandemic.

Indexed as

EcosystemInfluenza, HumanDisease OutbreaksGlobal HealthHumansPandemicsdata sharingdata transferdigital epidemiologydigital platformfluGlobal Flu Viewinfluenza-like illnessinnovativeparticipatory surveillanceprogrampublic healthsurveillance

Identifiers

PMID37490846
PMCPMC10504624
OpenAlexW4385256793

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.