000079640 001__ 79640
000079640 005__ 20200716101451.0
000079640 0247_ $$2doi$$a10.1371/journal.pcbi.1006173
000079640 0248_ $$2sideral$$a112243
000079640 037__ $$aART-2019-112243
000079640 041__ $$aeng
000079640 100__ $$aKalimeri, Kyriaki
000079640 245__ $$aUnsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptoms
000079640 260__ $$c2019
000079640 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079640 5203_ $$aSeasonal 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. Author 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.
000079640 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/MDM-2017-0711$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2017-87519-P$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 641191-CIMPLEX$$9info:eu-repo/grantAgreement/EC/H2020/641191/EU/Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories/CIMPLEX
000079640 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000079640 590__ $$a4.7$$b2019
000079640 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b6 / 59 = 0.102$$c2019$$dQ1$$eT1
000079640 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b9 / 77 = 0.117$$c2019$$dQ1$$eT1
000079640 592__ $$a2.91$$b2019
000079640 593__ $$aCellular and Molecular Neuroscience$$c2019$$dQ1
000079640 593__ $$aComputational Theory and Mathematics$$c2019$$dQ1
000079640 593__ $$aEcology$$c2019$$dQ1
000079640 593__ $$aMolecular Biology$$c2019$$dQ1
000079640 593__ $$aGenetics$$c2019$$dQ1
000079640 593__ $$aModeling and Simulation$$c2019$$dQ1
000079640 593__ $$aEcology, Evolution, Behavior and Systematics$$c2019$$dQ1
000079640 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000079640 700__ $$aDelfino, Matteo
000079640 700__ $$aCattuto, Ciro
000079640 700__ $$aPerrotta, Daniela
000079640 700__ $$aColizza, Vittoria
000079640 700__ $$aGuerrisi, Caroline
000079640 700__ $$aTurbelin, Clement
000079640 700__ $$aDuggan, Jim
000079640 700__ $$aEdmunds, John
000079640 700__ $$aObi, Chinelo
000079640 700__ $$aPebody, Richard
000079640 700__ $$aFranco, Ana O
000079640 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000079640 700__ $$aMeloni, Sandro
000079640 700__ $$aKoppeschaar, Carl
000079640 700__ $$aKjelso, Charlotte
000079640 700__ $$aMexia, Ricardo
000079640 700__ $$aPaolotti, Daniela
000079640 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000079640 773__ $$g15, 4 (2019), e100617 [21 p.]$$pPLoS Comput. Biol.$$tPLoS computational biology$$x1553-7358
000079640 8564_ $$s692863$$uhttps://zaguan.unizar.es/record/79640/files/texto_completo.pdf$$yVersión publicada
000079640 8564_ $$s95407$$uhttps://zaguan.unizar.es/record/79640/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000079640 909CO $$ooai:zaguan.unizar.es:79640$$particulos$$pdriver
000079640 951__ $$a2020-07-16-09:06:20
000079640 980__ $$aARTICLE