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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. ; 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). ; Peer reviewed

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. ; 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. ; 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 (MDM2017-0711 to the IFISC Institute). This work is partly supported by the UMR-S 1136/Public Health France partnership. ; info:eu-repo/semantics/publishedVersion

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.

As India moves toward the elimination of visceral leishmaniasis (VL) as a public health problem, comprehensive timely case detection has become increasingly important, in order to reduce the period of infectivity and control outbreaks. During the 2000s, localized research studies suggested that a large percentage of VL cases were never reported in government data. However, assessments conducted from 2013 to 2015 indicated that 85% or more of confirmed cases were eventually captured and reported in surveillance data, albeit with significant delays before diagnosis. Based on methods developed during these assessments, the CARE India team evolved new strategies for active case detection (ACD), applicable at large scale while being sufficiently effective in reducing time to diagnosis. Active case searches are triggered by the report of a confirmed VL case, and comprise two major search mechanisms: 1) case identification based on the index case's knowledge of other known VL cases and searches in nearby houses (snowballing); and 2) sustained contact over time with a range of private providers, both formal and informal. Simultaneously, house-to-house searches were conducted in 142 villages of 47 blocks during this period. We analyzed data from 5030 VL patients reported in Bihar from January 2018 through July 2019. Of these 3033 were detected passively and 1997 via ACD (15 (0.8%) via house-to-house and 1982 (99.2%) by light touch ACD methods). We constructed multinomial logistic regression models comparing time intervals to diagnosis (30-59, 60-89 and ≥90 days with =90 days compared to the referent of <30 days for ACD vs PCD were 0.88, 0.56 and 0.42 respectively. These ACD strategies not ...

As India moves toward the elimination of visceral leishmaniasis (VL) as a public health problem, comprehensive timely case detection has become increasingly important, in order to reduce the period of infectivity and control outbreaks. During the 2000s, localized research studies suggested that a large percentage of VL cases were never reported in government data. However, assessments conducted from 2013 to 2015 indicated that 85% or more of confirmed cases were eventually captured and reported in surveillance data, albeit with significant delays before diagnosis. Based on methods developed during these assessments, the CARE India team evolved new strategies for active case detection (ACD), applicable at large scale while being sufficiently effective in reducing time to diagnosis. Active case searches are triggered by the report of a confirmed VL case, and comprise two major search mechanisms: 1) case identification based on the index case's knowledge of other known VL cases and searches in nearby houses (snowballing); and 2) sustained contact over time with a range of private providers, both formal and informal. Simultaneously, house-to-house searches were conducted in 142 villages of 47 blocks during this period. We analyzed data from 5030 VL patients reported in Bihar from January 2018 through July 2019. Of these 3033 were detected passively and 1997 via ACD (15 (0.8%) via house-to-house and 1982 (99.2%) by light touch ACD methods). We constructed multinomial logistic regression models comparing time intervals to diagnosis (30-59, 60-89 and ≥90 days with =90 days compared to the referent of <30 days for ACD vs PCD were 0.88, 0.56 and 0.42 respectively. These ACD strategies not only reduce time to diagnosis, and thus risk of transmission, but also ensure that there is a double check on the proportion of cases actually getting captured. Such a process can supplement passive case detection efforts that must go on, possibly perpetually, even after elimination as a public health problem is achieved.