Browsing by Author "Cattuto, Ciro"
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- Social contact patterns during the COVID-19 pandemic in 21 European countries - evidence from a two-year studyPublication . Wong, Kerry L.M.; Gimma, Amy; Coletti, Pietro; Paolotti, Daniela; Tizzani, Michele; Cattuto, Ciro; Schmidt, Andrea; Gredinger, Gerald; Stumpfl, Sophie; Baruch, Joaquin; Melillo, Tanya; Hudeckova, Henrieta; Zibolenova, Jana; Chladna, Zuzana; Rosinska, Magdalena; Niedzwiedzka-Stadnik, Marta; Fischer, Krista; Vorobjov, Sigrid; Sõnajalg, Hanna; Althaus, Christian; Low, Nicola; Reichmuth, Martina; Auranen, Kari; Nurhonen, Markku; Petrović, Goranka; Makaric, Zvjezdana Lovric; Namorado, Sónia; Caetano, Constantino; Santos, Ana João; Röst, Gergely; Oroszi, Beatrix; Karsai, Márton; Fafangel, Mario; Klepac, Petra; Kranjec, Natalija; Vilaplana, Cristina; Casabona, Jordi; Faes, Christel; Beutels, Philippe; Hens, Niel; Jaeger, Veronika K.; Karch, Andre; Johnson, Helen; Jarvis, Christopher I.Background: Most countries have enacted some restrictions to reduce social contacts to slow down disease transmission during the COVID-19 pandemic. For nearly two years, individuals likely also adopted new behaviours to avoid pathogen exposure based on personal circumstances. We aimed to understand the way in which different factors affect social contacts - a critical step to improving future pandemic responses. Methods: The analysis was based on repeated cross-sectional contact survey data collected in a standardized international study from 21 European countries between March 2020 and March 2022. We calculated the mean daily contacts reported using a clustered bootstrap by country and by settings (at home, at work, or in other settings). Where data were available, contact rates during the study period were compared with rates recorded prior to the pandemic. We fitted censored individual-level generalized additive mixed models to examine the effects of various factors on the number of social contacts. Results: The survey recorded 463,336 observations from 96,456 participants. In all countries where comparison data were available, contact rates over the previous two years were substantially lower than those seen prior to the pandemic (approximately from over 10 to < 5), predominantly due to fewer contacts outside the home. Government restrictions imposed immediate effect on contacts, and these effects lingered after the restrictions were lifted. Across countries, the relationships between national policy, individual perceptions, or personal circumstances determining contacts varied. Conclusions: Our study, coordinated at the regional level, provides important insights into the understanding of the factors associated with social contacts to support future infectious disease outbreak responses.
- Unsupervised extraction of epidemic syndromes from participatory influenza surveillance self-reported symptomsPublication . Kalimeri, Kyriaki; Delfino, Matteo; Cattuto, Ciro; Perrotta, Daniela; Colizza, Vittoria; Guerrisi, Caroline; Turbelin, Clement; Duggan, Jim; Edmunds, John; Obi, Chinelo; Pebody, Richard; Franco, Ana O.; Moreno, Yamir; Meloni, Sandro; Koppeschaar, Carl; Kjelsø, Charlotte; Mexia, Ricardo; Paolotti, DanielaAbstract: 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.