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

Authors :
Ricardo Mexia
Sandro Meloni
Carl Koppeschaar
Chinelo Obi
Ana O. Franco
Kyriaki Kalimeri
Jim Duggan
Matteo Delfino
Daniela Paolotti
John Edmunds
C Kjelsø
Clément Turbelin
Daniela Perrotta
Vittoria Colizza
Ciro Cattuto
Yamir Moreno
Caroline Guerrisi
Richard Pebody
Data Science Laboratory (ISI)
ISI Foundation Institute for Scientific Interchange
Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP)
Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Sorbonne Université (SU)
National University of Ireland [Galway] (NUI Galway)
London School of Hygiene and Tropical Medicine (LSHTM)
Public Health England [London]
Instituto Gulbenkian de Ciência [Oeiras] (IGC)
Fundação Calouste Gulbenkian
University of Zaragoza - Universidad de Zaragoza [Zaragoza]
Instituto de fisic Interdisciplinar y Sistemas Complejos ( IFISC (CSIC-UIB))
Statens Serum Institut [Copenhagen]
Instituto Nacional de Saùde Dr Ricardo Jorge [Portugal] (INSA)
Agencia Estatal de Investigación (España)
European Commission
Institute for Scientific Interchange Foundation
Fondazione Cassa di Risparmio di Torino
Gobierno de Aragón
FENOL
Ministerio de Economía y Competitividad (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Source :
PLoS Computational Biology, PLoS Computational Biology, Public Library of Science, 2019, 15 (4), pp.e1006173. ⟨10.1371/journal.pcbi.1006173⟩, Zaguán. Repositorio Digital de la Universidad de Zaragoza, instname, PLoS Computational Biology, Vol 15, Iss 4, p e1006173 (2019), Digital.CSIC. Repositorio Institucional del CSIC
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

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.<br />The authors declare no competing financial interests. D.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 (MDM-2017-0711 to the IFISC Institute).

Details

Language :
English
ISSN :
20178751, 1553734X, and 15537358
Database :
OpenAIRE
Journal :
PLoS Computational Biology, PLoS Computational Biology, Public Library of Science, 2019, 15 (4), pp.e1006173. ⟨10.1371/journal.pcbi.1006173⟩, Zaguán. Repositorio Digital de la Universidad de Zaragoza, instname, PLoS Computational Biology, Vol 15, Iss 4, p e1006173 (2019), Digital.CSIC. Repositorio Institucional del CSIC
Accession number :
edsair.doi.dedup.....bf2f756b70eea5b9384ecab1ba8b3c46
Full Text :
https://doi.org/10.1371/journal.pcbi.1006173⟩