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Seasonal waves of influenza display a complex spatiotemporal pattern resulting from the interplay of biological, sociodemographic, and environmental factors. At country level many studies characterized the robust properties of annual epidemics, depicting a typical season. Here we analyzed season-by-season variability, introducing a clustering approach to assess the deviations from typical spreading patterns. The classification is performed on the similarity of temporal configurations of onset and peak times of regional epidemics, based on influenza-like-illness time-series in France from 1984 to 2014. We observed a larger variability in the onset compared to the peak. Two relevant classes of clusters emerge: groups of seasons sharing similar recurrent spreading patterns (clustered seasons) and single seasons displaying unique patterns (monoids). Recurrent patterns exhibit a more pronounced spatial signature than unique patterns. We assessed how seasons shift between these classes from onset to peak depending on epidemiological, environmental, and socio-demographic variables. We found that the spatial dynamics of influenza and its association with commuting, previously observed as a general property of French influenza epidemics, apply only to seasons exhibiting recurrent patterns. The proposed methodology is successful in providing new insights on influenza spread and can be applied to incidence time-series of different countries and different diseases. ; The work was partially supported by the EC-Health contract no. 278433 (PREDEMICS) to PC, CP and VC and by the French ANR project HarMS-flu (ANR-12-MONU-0018) to PC and VC. PC also acknowledges partial support from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement 682540 - TransMID).

International audience ; Influenza surveillance systems aim to annually track influenza epidemics, detecting their start, monitoring their spatio-temporal spread, identifying populations at risk and circulating viruses, and estimating the impact on the community and healthcare structures [1]. Sentinel surveillance systems are based on primary care and report the weekly number of patients examined with influenza-related illness. Recently, a variety of non-traditional surveillance approaches have emerged [ 2-9] where large amounts of crowdsourced digital data produced by individuals enable such indi-viduals to contribute to monitoring the health of their community and to provide authorities with additional characterisations of the epidemic. In the European Union, more than one third of the countries run a participatory surveillance system to monitor influenza epidemics, under the standardised framework of the Influenzanet network established in 2011 [2,10,11]. The Influenzanet network is a syndromic surveillance system based on voluntary self-reporting of symptoms by participants residing in countries, which are part of the Influenzanet.

International audience ; Influenza surveillance systems aim to annually track influenza epidemics, detecting their start, monitoring their spatio-temporal spread, identifying populations at risk and circulating viruses, and estimating the impact on the community and healthcare structures [1]. Sentinel surveillance systems are based on primary care and report the weekly number of patients examined with influenza-related illness. Recently, a variety of non-traditional surveillance approaches have emerged [ 2-9] where large amounts of crowdsourced digital data produced by individuals enable such indi-viduals to contribute to monitoring the health of their community and to provide authorities with additional characterisations of the epidemic. In the European Union, more than one third of the countries run a participatory surveillance system to monitor influenza epidemics, under the standardised framework of the Influenzanet network established in 2011 [2,10,11]. The Influenzanet network is a syndromic surveillance system based on voluntary self-reporting of symptoms by participants residing in countries, which are part of the Influenzanet.

International audience ; BackgroundThe Internet is becoming more commonly used as a tool for disease surveillance. Similarly to other surveillance systems and to studies using online data collection, Internet-based surveillance will have biases in participation, affecting the generalizability of the results. Here we quantify the participation biases of Influenzanet, an ongoing European-wide network of Internet-based participatory surveillance systems for influenza-like-illness.MethodsIn 2011/2012 Influenzanet launched a standardized common framework for data collection applied to seven European countries. Influenzanet participants were compared to the general population of the participating countries to assess the representativeness of the sample in terms of a set of demographic, geographic, socio-economic and health indicators.ResultsMore than 30,000 European residents registered to the system in the 2011/2012 season, and a subset of 25,481 participants were selected for this study. All age classes (10 years brackets) were represented in the cohort, including under 10 and over 70 years old. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and oldest age classes. The gender imbalance differed between countries. A counterbalance between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may be at the origin of this difference. Once adjusted by demographic indicators, a similar propensity to commute was observed for each country, and the same top three transportation modes were used for six countries out of seven. Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries.ConclusionsExisting demographic and national datasets allowed the quantification ...

International audience ; BackgroundThe Internet is becoming more commonly used as a tool for disease surveillance. Similarly to other surveillance systems and to studies using online data collection, Internet-based surveillance will have biases in participation, affecting the generalizability of the results. Here we quantify the participation biases of Influenzanet, an ongoing European-wide network of Internet-based participatory surveillance systems for influenza-like-illness.MethodsIn 2011/2012 Influenzanet launched a standardized common framework for data collection applied to seven European countries. Influenzanet participants were compared to the general population of the participating countries to assess the representativeness of the sample in terms of a set of demographic, geographic, socio-economic and health indicators.ResultsMore than 30,000 European residents registered to the system in the 2011/2012 season, and a subset of 25,481 participants were selected for this study. All age classes (10 years brackets) were represented in the cohort, including under 10 and over 70 years old. The Influenzanet population was not representative of the general population in terms of age distribution, underrepresenting the youngest and oldest age classes. The gender imbalance differed between countries. A counterbalance between gender-specific information-seeking behavior (more prominent in women) and Internet usage (with higher rates in male populations) may be at the origin of this difference. Once adjusted by demographic indicators, a similar propensity to commute was observed for each country, and the same top three transportation modes were used for six countries out of seven. Smokers were underrepresented in the majority of countries, as were individuals with diabetes; the representativeness of asthma prevalence and vaccination coverage for 65+ individuals in two successive seasons (2010/2011 and 2011/2012) varied between countries.ConclusionsExisting demographic and national datasets allowed the quantification of the participation biases of a large cohort for influenza-like-illness surveillance in the general population. Significant differences were found between Influenzanet participants and the general population. The quantified biases need to be taken into account in the analysis of Influenzanet epidemiological studies and provide indications on populations groups that should be targeted in recruitment efforts.

We measured COVID-19 vaccine effectiveness (VE) against symptomatic SARS-CoV-2 infection at primary care/outpatient level among adults ≥ 65 years old using a multicentre test-negative design in eight European countries. We included 592 SARS-CoV-2 cases and 4,372 test-negative controls in the main analysis. The VE was 62% (95% CI: 45-74) for one dose only and 89% (95% CI: 79-94) for complete vaccination. COVID-19 vaccines provide good protection against COVID-19 presentation at primary care/outpatient level, particularly among fully vaccinated individuals. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101003673. ; Sí

As the COVID-19 pandemic began in early 2020, primary care influenza sentinel surveillance networks within the Influenza - Monitoring Vaccine Effectiveness in Europe (I-MOVE) consortium rapidly adapted to COVID-19 surveillance. This study maps system adaptations and lessons learned about aligning influenza and COVID-19 surveillance following ECDC / WHO/Europe recommendations and preparing for other diseases possibly emerging in the future. Using a qualitative approach, we describe the adaptations of seven sentinel sites in five European Union countries and the United Kingdom during the first pandemic phase (March–September 2020). Adaptations to sentinel systems were substantial (2/7 sites), moderate (2/7) or minor (3/7 sites). Most adaptations encompassed patient referral and sample collection pathways, laboratory testing and data collection. Strengths included established networks of primary care providers, highly qualified testing laboratories and stakeholder commitments. One challenge was the decreasing number of samples due to altered patient pathways. Lessons learned included flexibility establishing new routines and new laboratory testing. To enable simultaneous sentinel surveillance of influenza and COVID-19, experiences of the sentinel sites and testing infrastructure should be considered. The contradicting aims of rapid case finding and contact tracing, which are needed for control during a pandemic and regular surveillance, should be carefully balanced.

I-MOVE-COVID-19 primary care study team (in addition to authors above): Nick Andrews, Jamie Lopez Bernal, Heather Whitaker, Caroline Guerrisi, Titouan Launay, Shirley Masse, Sylvie van der Werf, Vincent Enouf, John Cuddihy, Adele McKenna, Michael Joyce, Cillian de Gascun, Joanne Moran, Ana Miqueleiz, Ana Navascués, Camino Trobajo-Sanmartín, Carmen Ezpeleta, Paula López Moreno, Javier Gorricho, Eva Ardanaz, Fernando Baigorria, Aurelio Barricarte, Enrique de la Cruz, Nerea Egüés, Manuel García Cenoz, Marcela Guevara, Conchi Moreno-Iribas, Carmen Sayón, Verónica Gomez, Baltazar Nunes, Rita Roquete, Adriana Silva, Aryse Melo, Inês Costa, Nuno Verdasca, Patrícia Conde, Diogo FP Marques, Anna Molesworth, Leanne Quinn, Miranda Leyton, Selin Campbell, Janine Thoulass, Jim McMenamin, Ana Martínez Mateo, Luca Basile, Daniel Castrillejo, Carmen Quiñones Rubio, Concepción Delgado-Sanz, Jesús Oliva. ; The I-MOVE-COVID-19 network collates epidemiological and clinical information on patients with coronavirus disease (COVID-19), including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virological characterisation in 11 European countries [1]. One component of I-MOVE-COVID-19 is the multicentre vaccine effectiveness (VE) study at primary care/outpatient level in nine European study sites in eight countries. We measured overall and product-specific COVID-19 VE against symptomatic SARS-CoV-2 infection among those aged 65 years and older. We also measured VE by time since vaccination. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101003673. ; info:eu-repo/semantics/publishedVersion