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Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms

2019-04-08Journal article Open access
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dc.contributor.authorKalimeri, Kyriaki
dc.contributor.authorDelfino, Matteo
dc.contributor.authorCattuto, Ciro
dc.contributor.authorPerrotta, Daniela
dc.contributor.authorColizza, Vittoria
dc.contributor.authorGuerrisi, Caroline
dc.contributor.authorTurbelin, Clement
dc.contributor.authorDuggan, Jim
dc.contributor.authorEdmunds, John
dc.contributor.authorObi, Chinelo
dc.contributor.authorPebody, Richard
dc.contributor.authorFranco, Ana O.
dc.contributor.authorMoreno, Yamir
dc.contributor.authorMeloni, Sandro
dc.contributor.authorKoppeschaar, Carl
dc.contributor.authorKjelsø, Charlotte
dc.contributor.authorMexia, Ricardo
dc.contributor.authorPaolotti, Daniela
dc.date.accessioned2019-05-02T17:28:30Z
dc.date.available2019-05-02T17:28:30Z
dc.date.issued2019-04-08
dc.description.abstractAbstract: Seasonal influenza surveillance is usually carried out by sentinel general practitioners (GPs) who compile weekly reports based on the number of influenza-like illness (ILI) clinical cases observed among visited patients. This traditional practice for surveillance generally presents several issues, such as a delay of one week or more in releasing reports, population biases in the health-seeking behaviour, and the lack of a common definition of ILI case. On the other hand, the availability of novel data streams has recently led to the emergence of non-traditional approaches for disease surveillance that can alleviate these issues. In Europe, a participatory web-based surveillance system called Influenzanet represents a powerful tool for monitoring seasonal influenza epidemics thanks to aid of self-selected volunteers from the general population who monitor and report their health status through Internet-based surveys, thus allowing a real-time estimate of the level of influenza circulating in the population. In this work, we propose an unsupervised probabilistic framework that combines time series analysis of self-reported symptoms collected by the Influenzanet platforms and performs an algorithmic detection of groups of symptoms, called syndromes. The aim of this study is to show that participatory web-based surveillance systems are capable of detecting the temporal trends of influenza-like illness even without relying on a specific case definition. The methodology was applied to data collected by Influenzanet platforms over the course of six influenza seasons, from 2011-2012 to 2016-2017, with an average of 34,000 participants per season. Results show that our framework is capable of selecting temporal trends of syndromes that closely follow the ILI incidence rates reported by the traditional surveillance systems in the various countries (Pearson correlations ranging from 0.69 for Italy to 0.88 for the Netherlands, with the sole exception of Ireland with a correlation of 0.38). The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season (2016-2017) based only on the available information of the previous years (2011-2016). Furthermore, to broaden the scope of our approach, we applied it both in a forecasting fashion to predict the ILI trend of the 2016-2017 influenza season (Pearson correlations ranging from 0.60 for Ireland and UK, and 0.85 for the Netherlands) and also to detect gastrointestinal syndrome in France (Pearson correlation of 0.66). The final result is a near-real-time flexible surveillance framework not constrained by any specific case definition and capable of capturing the heterogeneity in symptoms circulation during influenza epidemics in the various European countries.pt_PT
dc.description.abstractAuthor summary: This study suggests how web-based surveillance data can provide an epidemiological signal capable of detecting the temporal trends of influenza-like illness without relying on a specific case definition. The proposed framework was able to forecast quite accurately the ILI trend of the forthcoming influenza season based only on the available information of the previous years. Moreover, to broaden the scope of our approach, we applied it to the detection of gastrointestinal syndromes. We evaluated the approach against the traditional surveillance data and despite the limited amount of data, the gastrointestinal trend was successfully detected. The result is a near-real-time flexible surveillance and prediction tool that is not constrained by any disease case definition.pt_PT
dc.description.sponsorshipD.Pa. and D.Pe. acknowledge support from H2020 FETPROACT-GSS CIMPLEX Grant No. 641191. KK, CC, D.Pa., D.Pe., Y.M. and M.D. acknowledge support from the Lagrange Project of the Institute for Scientific Interchange Foundation (ISI Foundation) funded by Fondazione Cassa di Risparmio di Torino (Fondazione CRT). Y.M. acknowledges support from the Government of Aragon, Spain through a grant to the group FENOL and by Ministry of Economy and Competitiveness (MINECO) and European Regional Development Fund (FEDER) (Grant No. FIS2017-87519-P). S.M. acknowledges support from the Spanish State Research Agency, through the María de Maeztu Program for Units of Excellence in R&D (MDM2017-0711 to the IFISC Institute). This work is partly supported by the UMR-S 1136/Public Health France partnership.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPLoS Comput Biol. 2019 Apr 8;15(4):e1006173. doi: 10.1371/journal.pcbi.1006173. eCollection 2019 Aprpt_PT
dc.identifier.doi10.1371/journal.pcbi.1006173pt_PT
dc.identifier.issn1553-734X
dc.identifier.urihttp://hdl.handle.net/10400.18/6382
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherPublic Library of Science/ International Society for Computational Biology (ISCB)pt_PT
dc.relation.publisherversionhttps://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006173pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectinfluenzapt_PT
dc.subjectInfluenza-like illness (ILI)pt_PT
dc.subjectSurveillancept_PT
dc.subjectEpidemiological Surveillancept_PT
dc.subjectEstados de Saúde e de Doençapt_PT
dc.subjectObservação em Saúde e Vigilânciapt_PT
dc.subjectCuidados de Saúdept_PT
dc.titleUnsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptomspt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue4pt_PT
oaire.citation.startPagee1006173pt_PT
oaire.citation.titlePLoS Computational Biologypt_PT
oaire.citation.volume15pt_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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