The potential value of crowdsourced surveillance systems in supplementing sentinel influenza networks: the case of France

What are the characteristics of participatory surveillance systems for influenza-like-illness?

Background

Seasonal influenza causes significant morbidity and mortality, with an estimated 9.4 million hospitalisations and 290 000-650 000 respiratory related-deaths globally each year. Influenza can also cause mild illness, which is why not all symptomatic persons might necessarily be tested for influenza. To monitor influenza activity, healthcare facility-based syndromic surveillance for influenza-like illness is often implemented. Participatory surveillance systems for influenza-like illness (ILI) play an important role in influenza surveillance and can complement traditional facility-based surveillance systems to provide real-time estimates of influenza-like illness activity. However, such systems differ in designs between countries and contexts, making it necessary to identify their characteristics to better understand how they fit traditional surveillance systems. Consequently, we aimed to investigate the performance of participatory surveillance systems for ILI worldwide.

Methods

We systematically searched four databases for relevant articles on influenza participatory surveillance systems for ILI. We extracted data from the included, eligible studies and assessed their quality using the Joanna Briggs Critical Appraisal Tools. We then synthesised the findings using narrative synthesis.

Results

We included 39 out of 3797 retrieved articles for analysis. We identified 26 participatory surveillance systems, most of which sought to capture the burden and trends of influenza-like illness and acute respiratory infections among cohorts with risk factors for influenza-like illness. Of all the surveillance system attributes assessed, 52% reported on correlation with other surveillance systems, 27% on representativeness, and 21% on acceptability. Among studies that reported these attributes, all systems were rated highly in terms of simplicity, flexibility, sensitivity, utility, and timeliness. Most systems (87.5%) were also well accepted by users, though participation rates varied widely. However, despite their potential for greater reach and accessibility, most systems (90%) fared poorly in terms of representativeness of the population. Stability was a concern for some systems (60%), as was completeness (50%).

Conclusions

The analysis of participatory surveillance system attributes showed their potential in providing timely and reliable influenza data, especially in combination with traditional hospital- and laboratory led-surveillance systems. Further research is needed to design future systems with greater uptake and utility.

Comprehensive surveillance of acute respiratory infections during the COVID-19 pandemic: a methodological approach using sentinel networks, Castilla y León, Spain, January 2020 to May 2022

BackgroundSince 1996, epidemiological surveillance of acute respiratory infections (ARI) in Spain has been limited to seasonal influenza, respiratory syncytial virus (RSV) and potential pandemic viruses. The COVID-19 pandemic provides opportunities to adapt existing systems for extended surveillance to capture a broader range of ARI.AimTo describe how the Influenza Sentinel Surveillance System of Castilla y León, Spain was rapidly adapted in 2020 to comprehensive sentinel surveillance for ARI, including influenza and COVID-19.MethodsUsing principles and methods of the health sentinel network, we integrated electronic medical record data from 68 basic surveillance units, covering 2.6% of the regional population between January 2020 to May 2022. We tested sentinel and non-sentinel samples sent weekly to the laboratory network for SARS-CoV-2, influenza viruses and other respiratory pathogens. The moving epidemic method (MEM) was used to calculate epidemic thresholds.ResultsARI incidence was estimated at 18,942 cases per 100,000 in 2020/21 and 45,223 in 2021/22, with similar seasonal fold increases by type of respiratory disease. Incidence of influenza-like illness was negligible in 2020/21 but a 5-week epidemic was detected by MEM in 2021/22. Epidemic thresholds for ARI and COVID-19 were estimated at 459.4 and 191.3 cases per 100,000 population, respectively. More than 5,000 samples were tested against a panel of respiratory viruses in 2021/22.ConclusionExtracting data from electronic medical records reported by trained professionals, combined with a standardised microbiological information system, is a feasible and useful method to adapt influenza sentinel reports to comprehensive ARI surveillance in the post-COVID-19 era.

Epidemiology of acute gastroenteritis in France from November 2019-August 2021, in light of reported adherence to COVID-19 barrier measures

Since the start of the COVID-19 pandemic, French health authorities have encouraged barrier measures and implemented three lockdowns to slow SARS-CoV-2 transmission. We aimed to examine the impact of these measures on the epidemiology of acute gastroenteritis (AGE) in France, from November 2019 to August 2021. We describe trends in AGE indicators from syndromic surveillance and a sentinel surveillance network. Additionally, we describe reported AGE illness data from a community based cohort, and frequencies of adherence to COVID-19 barrier measures from repeated quantitative surveys. From week 7 in 2020, all AGE indicators reached the lowest levels observed since the last decade. During the first lockdown, the median incidence rate reported by the sentinel network was 32 per 100,000 inhabitants, 1.9 times lower than the minimum registered during the 2010-2019 period. Low activity persisted until April 2021. Reported illness from the community cohort mirrored these trends. Adherence to COVID-19 barrier measures was highest during the first lockdown, coinciding with the steep decrease in AGE incidence. Among children under 5 years, AGE incidence increased after the third lockdown in June and July 2021, but remained lower than previous winter-season peaks. Our study indicates that a reduction in adherence to COVID-19 barrier measures, and the end of the lockdowns, coincided with an increase in AGE incidence, particularly among young children. We therefore strongly recommend maintaining adherence to barrier measures in order to in order to limit the transmission of AGE related pathogens.

Concordance between the Clinical Diagnosis of Influenza in Primary Care and Epidemiological Surveillance Systems (PREVIGrip Study)

Introduction: Health authorities use different systems of influenza surveillance. Sentinel networks, which are recommended by the World Health Organization, provide information on weekly influenza incidence in a monitored population, based on laboratory-confirmed cases. In Catalonia there is a public website, DiagnostiCat, that publishes the number of weekly clinical diagnoses at the end of each week of disease registration, while the sentinel network publishes its reports later. The objective of this study was to determine whether there is concordance between the number of cases of clinical diagnoses and the number of confirmed cases of influenza, in order to evaluate the predictive potential of a clinical diagnosis-based system. Methods: Population-based ecological time series study in Catalonia. The period runs from the 2010–2011 to the 2018–2019 season. The concordance between the clinical diagnostic cases and the confirmed cases was evaluated. The degree of agreement and the concordance were analysed using Bland–Altman graphs and intraclass correlation coefficients. Results: There was greater concordance between the clinical diagnoses and the sum of the cases confirmed outside and within the sentinel network than between the diagnoses and the confirmed sentinel cases. The degree of agreement was higher when influenza rates were low. Conclusions: There is concordance between the clinical diagnosis and the confirmed cases of influenza. Registered clinical diagnostic cases could provide a good alternative to traditional surveillance, based on case confirmation. Cases of clinical diagnosis of influenza may have the potential to predict the onset of annual influenza epidemics.

Perceived risk of opioid use disorder secondary to opioid analgesic medication use by the general population in France

BACKGROUND

In Europe and France, the use of opioid analgesic drugs has become widespread as an option for pain management. However, their use can lead to nonmedical use and/or opioid use disorder (OUD). This work aimed to assess the perceived risk of OUD secondary to opioid analgesic drugs use by the general population.

METHODS

We conducted a cross-sectional observational study using the GrippeNet web-based cohort, comprising about 10,000 French volunteers from the general population, using a self-administered questionnaire. The main outcome was the perceived risk of OUD secondary to opioid analgesic drugs use, assessed by a 4-item scale and modeled using logistic regression (backward procedure).

RESULTS

Among 5,046 French respondents, after adjustment, 65% believed that the use of analgesic drugs could likely or very likely lead to OUD. Factors associated with perception of a higher risk were being over 50 and having heard about opioids in the media. Previous opioid use and a high level of education decreased the perception of the risk. Among those having used opioids in the past two years (N = 1770), 71.1% reported being not at all concerned by this risk. The majority of the sample perceived the risk of OUD but those having already used opioid analgesics drugs expressed no concern about this risk for themselves.

CONCLUSIONS

This finding highlight the need to reinforce warning on the package insert documents, therapeutic education and collaborative care between the prescribing general practitioners and pharmacists to increase awareness of opioid medications users on the risk of OUD.

Testing behaviour and positivity for SARS-CoV-2 infection: insights from web-based participatory surveillance in the Netherlands

OBJECTIVES

We aimed to identify populations at a high risk for SARS-CoV-2 infection but who are less likely to present for testing, by determining which sociodemographic and household factors are associated with a lower propensity to be tested and, if tested, with a higher risk of a positive test result.

DESIGN AND SETTING

Internet-based participatory surveillance data from the general population of the Netherlands.

PARTICIPANTS

Weekly survey data collected over a 5-month period (17 November 2020 to 18 April 2021) from a total of 12 026 participants who had contributed at least 2 weekly surveys was analysed.

METHODS

Multivariable analyses using generalised estimating equations for binomial outcomes were conducted to estimate the adjusted ORs of testing and of test positivity associated with participant and household characteristics.

RESULTS

Male sex (adjusted OR for testing (OR_t): 0.92; adjusted OR for positivity (OR_p): 1.30, age groups<20 (OR_t: 0.89; OR_p: 1.27), 50-64 years (OR_t: 0.94; OR_p: 1.06) and 65+ years (OR_t: 0.78; OR_p: 1.24), diabetics (OR_t: 0.97; OR_p: 1.06) and sales/administrative employees (OR_t: 0.93; OR_p: 1.90) were distinguished as lower test propensity/higher test positivity factors.

CONCLUSIONS

The factors identified using this approach can help identify potential target groups for improving communication and encouraging testing among those with symptoms, and thus increase the effectiveness of testing, which is essential for the response to the COVID-19 pandemic and for public health strategies in the longer term.

No Change of Pneumocystis jirovecii Pneumonia after the COVID-19 Pandemic: Multicenter Time-Series Analyses

Consolidated infection control measures imposed by the government and hospitals during COVID-19 pandemic resulted in a sharp decline of respiratory viruses. Based on the issue of whether Pneumocystis jirovecii could be transmitted by airborne and acquired from the environment, we assessed changes in P. jirovecii pneumonia (PCP) cases in a hospital setting before and after COVID-19. We retrospectively collected data of PCP-confirmed inpatients aged ≥18 years (N = 2922) in four university-affiliated hospitals between January 2015 and June 2021. The index and intervention dates were defined as the first time of P. jirovecii diagnosis and January 2020, respectively. We predicted PCP cases for post-COVID-19 and obtained the difference (residuals) between forecasted and observed cases using the autoregressive integrated moving average (ARIMA) and the Bayesian structural time-series (BSTS) models. Overall, the average of observed PCP cases per month in each year were 36.1 and 47.3 for pre- and post-COVID-19, respectively. The estimate for residuals in the ARIMA model was not significantly different in the total PCP-confirmed inpatients (7.4%, p = 0.765). The forecasted PCP cases by the BSTS model were not significantly different from the observed cases in the post-COVID-19 (−0.6%, 95% credible interval; −9.6~9.1%, p = 0.450). The unprecedented strict non-pharmacological interventions did not affect PCP cases.

Common communicable diseases in the general population in France during the COVID-19 pandemic

In France, social distancing measures have been adopted to contain the spread of COVID-19, culminating in national Lockdowns. The use of hand washing, hydro-alcoholic rubs and mask-wearing also increased over time. As these measures are likely to impact the transmission of many communicable diseases, we studied the changes in common infectious diseases incidence in France during the first year of COVID-19 circulation. We examined the weekly incidence of acute gastroenteritis, chickenpox, acute respiratory infections and bronchiolitis reported in general practitioner networks since January 2016. We obtained search engine query volume for French terms related to these diseases and sales data for relevant drugs over the same period. A periodic regression model was fit to disease incidence, drug sales and search query volume before the COVID-19 period and extrapolated afterwards. We compared the expected values with observations made in 2020. During the first lockdown period, incidence dropped by 67% for gastroenteritis, by 79% for bronchiolitis, by 49% for acute respiratory infection and 90% for chickenpox compared to the past years. Reductions with respect to the expected incidence reflected the strength of implemented measures. Incidence in children was impacted the most. Reduction in primary care consultations dropped during a short period at the beginning of the first lockdown period but remained more than 95% of the expected value afterwards. In primary care, the large decrease in reported gastroenteritis, chickenpox or bronchiolitis observed during the period where many barrier measures were implemented imply that the circulation of common viruses was reduced and informs on the overall effect of these measures.

Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta

Background

Seasonal influenza has major implications for healthcare services as outbreaks often lead to high activity levels in health systems. Being able to predict when such outbreaks occur is vital. Mathematical models have extensively been used to predict epidemics of infectious diseases such as seasonal influenza and to assess effectiveness of control strategies. Availability of comprehensive and reliable datasets used to parametrize these models is limited. In this paper we combine a unique epidemiological dataset collected in Malta through General Practitioners (GPs) with a novel method using cross-sectional surveys to study seasonal influenza dynamics in Malta in 2014–2016, to include social dynamics and self-perception related to seasonal influenza.

Methods

Two cross-sectional public surveys (n = 406 per survey) were performed by telephone across the Maltese population in 2014–15 and 2015–16 influenza seasons. Survey results were compared with incidence data (diagnosed seasonal influenza cases) collected by GPs in the same period and with Google Trends data for Malta. Information was collected on whether participants recalled their health status in past months, occurrences of influenza symptoms, hospitalisation rates due to seasonal influenza, seeking GP advice, and other medical information.

Results

We demonstrate that cross-sectional surveys are a reliable alternative data source to medical records. The two surveys gave comparable results, indicating that the level of recollection among the public is high. Based on two seasons of data, the reporting rate in Malta varies between 14 and 22%. The comparison with Google Trends suggests that the online searches peak at about the same time as the maximum extent of the epidemic, but the public interest declines and returns to background level. We also found that the public intensively searched the Internet for influenza-related terms even when number of cases was low.

Conclusions

Our research shows that a telephone survey is a viable way to gain deeper insight into a population’s self-perception of influenza and its symptoms and to provide another benchmark for medical statistics provided by GPs and Google Trends. The information collected can be used to improve epidemiological modelling of seasonal influenza and other infectious diseases, thus effectively contributing to public health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11862-x.

Home testing for COVID-19 and other virus outbreaks: The complex system of translating to communities

Home testing is an emerging innovation that can enable nations and health care systems to safely and efficiently test large numbers of patients to manage COVID-19 and other viral outbreaks.  In this position paper, we explore the process of moving home testing across the translational continuum from labs to households, and ultimately into practice and communities for optimal public health impact. We focus on the four translational science drivers to accelerate the implementation of systems-wide home testing programmes 1) collaboration and team science, 2) technology, 3) multilevel interventions, and 4) knowledge integration. We use the Socio Ecological Model (SEM) as a framework to illustrate our vision for the ideal future state of a comprehensive system of stakeholders utilising tech-enabled home testing for COVID-19 and other virus outbreaks, and we suggest SEM as a tool to address key translational readiness and response questions.

Risk factors associated with the incidence of self-reported COVID-19-like illness: data from a web-based syndromic surveillance system in the Netherlands

During the first wave of the severe acute respiratory syndrome-coronavirus-2 epidemic in the Netherlands, notifications consisted mostly of patients with relatively severe disease. To enable real-time monitoring of the incidence of mild coronavirus disease 2019 (COVID-19) – for which medical consultation might not be required – the Infectieradar web-based syndromic surveillance system was launched in mid-March 2020. Our aim was to quantify associations between Infectieradar participant characteristics and the incidence of self-reported COVID-19-like illness. Recruitment for this cohort study was via a web announcement. After registering, participants completed weekly questionnaires, reporting the occurrence of a set of symptoms. The incidence rate of COVID-19-like illness was estimated and multivariable Poisson regression used to estimate the relative risks associated with sociodemographic variables, lifestyle factors and pre-existing medical conditions. Between 17 March and 24 May 2020, 25 663 active participants were identified, who reported 7060 episodes of COVID-19-like illness over 131 404 person-weeks of follow-up. The incidence rate declined over the analysis period, consistent with the decline in notified cases. Male sex, age 65+ years and higher education were associated with a significantly lower COVID-19-like illness incidence rate (adjusted rate ratios (RRs) of 0.80 (95% CI 0.76–0.84), 0.77 (0.70–0.85), 0.84 (0.80–0.88), respectively) and the baseline characteristics ever-smoker, asthma, allergies, diabetes, chronic lung disease, cardiovascular disease and children in the household were associated with a higher incidence (RRs of 1.11 (1.04–1.19) to 1.69 (1.50–1.90)). Web-based syndromic surveillance has proven useful for monitoring the temporal trends in, and risk factors associated with, the incidence of mild disease. Increased relative risks observed for several patient factors could reflect a combination of exposure risk, susceptibility to infection and propensity to report symptoms.

Postcode based participatory disease surveillance systems : a comparison with traditional risk-based surveillance and its application in the COVID-19 pandemic

BACKGROUND

Background: The SARS-Cov-2 infection has rapidly saturated health systems and traditional surveillance networks are finding hard to keep pace with its spread. We designed a participatory disease surveillance (PDS) system, to capture symptoms of Influenza-like illness (ILI) to estimate SARS-CoV-2 infection in the community. While data generated by these platforms can help public health organisations find community hotspots and effectively direct control measures, it has never been compared to traditional systems.

OBJECTIVE

Methods and Objectives: A completely anonymised web based PDS system, www.trackcovid-19.org was developed. We evaluated the symptomatic responses received form the PDS system to the traditional risk based surveillance carried out by the Bruhat Bengaluru Mahanagara Palike over a period of 45 days in the South Indian city of Bengaluru.

METHODS

Methods and Objectives: A completely anonymised web based PDS system, www.trackcovid-19.org was developed. We evaluated the symptomatic responses received form the PDS system to the traditional risk based surveillance carried out by the Bruhat Bengaluru Mahanagara Palike over a period of 45 days in the South Indian city of Bengaluru.

RESULTS

Results: The PDS system recorded 11062 entries from 106 Postal codes. A healthy response was obtained from 10863 users while 199 (1.8%) reported symptomatic. Subgroup analysis of a 14 day symptomatic window recorded 33 (0.29%) responses. Risk based surveillance was carried out covering a population of 605,284 with 209 (0.03%) individuals identified symptomatic.

CONCLUSIONS

Conclusion: Web PDS platforms provide better visualisation of community infection when compared to traditional risk based surveillance systems. They are extremely useful by providing real time information in the extended battle against this pandemic. When integrated into national disease surveillance systems, they can provide long term community surveillance adding an important cost-effective layer to already available data sources.

Underdetection of cases of COVID-19 in France threatens epidemic control

As countries in Europe gradually relaxed lockdown restrictions after the first wave, test-trace-isolate strategies became critical to maintain the incidence of coronavirus disease 2019 (COVID-19) at low levels^1,2. Reviewing their shortcomings can provide elements to consider in light of the second wave that is currently underway in Europe. Here we estimate the rate of detection of symptomatic cases of COVID-19 in France after lockdown through the use of virological³ and participatory syndromic⁴ surveillance data coupled with mathematical transmission models calibrated to regional hospitalizations². Our findings indicate that around 90,000 symptomatic infections, corresponding to 9 out 10 cases, were not ascertained by the surveillance system in the first 7 weeks after lockdown from 11 May to 28 June 2020, although the test positivity rate did not exceed the 5% recommendation of the World Health Organization (WHO)⁵. The median detection rate increased from 7% (95% confidence interval, 6-8%) to 38% (35-44%) over time, with large regional variations, owing to a strengthening of the system as well as a decrease in epidemic activity. According to participatory surveillance data, only 31% of individuals with COVID-19-like symptoms consulted a doctor in the study period. This suggests that large numbers of symptomatic cases of COVID-19 did not seek medical advice despite recommendations, as confirmed by serological studies^6,7. Encouraging awareness and same-day healthcare-seeking behaviour of suspected cases of COVID-19 is critical to improve detection. However, the capacity of the system remained insufficient even at the low epidemic activity achieved after lockdown, and was predicted to deteriorate rapidly with increasing incidence of COVID-19 cases. Substantially more aggressive, targeted and efficient testing with easier access is required to act as a tool to control the COVID-19 pandemic. The testing strategy will be critical to enable partial lifting of the current restrictive measures in Europe and to avoid a third wave.

Elucidating user behaviours in a digital health surveillance system to correct prevalence estimates

Estimating seasonal influenza prevalence is of undeniable public health importance, but remains challenging with traditional datasets due to cost and timeliness. Digital epidemiology has the potential to address this challenge, but can introduce sampling biases that are distinct to traditional systems. In online participatory health surveillance systems, the voluntary nature of the data generating process must be considered to address potential biases in estimates. Here we examine user behaviours in one such platform, FluTracking, from 2011 to 2017. We build a Bayesian model to estimate probabilities of an individual reporting in each week, given their past reporting behaviour, and to infer the weekly prevalence of influenza-like-illness (ILI) in Australia. We show that a model that corrects for user behaviour can substantially affect ILI estimates. The model examined here elucidates several factors, such as the status of having ILI and consistency of prior reporting, that are strongly associated with the likelihood of participating in online health surveillance systems. This framework could be applied to other digital participatory health systems where participation is inconsistent and sampling bias may be of concern.

Usefulness of Clinical Definitions of Influenza for Public Health Surveillance Purposes

This study investigated the performance of various case definitions and influenza symptoms in a primary healthcare sentinel surveillance system. A retrospective study of the clinical and epidemiological characteristics of the cases reported by a primary healthcare sentinel surveillance network for eleven years in Catalonia was conducted. Crude and adjusted diagnostic odds ratios (aDORs) and 95% confidence intervals (CIs) of the case definitions and symptoms for all weeks and epidemic weeks were estimated. The most predictive case definition for laboratory-confirmed influenza was the World Health Organization (WHO) case definition for ILI in all weeks (aDOR 2.69; 95% CI 2.42–2.99) and epidemic weeks (aDOR 2.20; 95% CI 1.90–2.54). The symptoms that were significant positive predictors for confirmed influenza were fever, cough, myalgia, headache, malaise, and sudden onset. Fever had the highest aDOR in all weeks (4.03; 95% CI 3.38–4.80) and epidemic weeks (2.78; 95% CI 2.21–3.50). All of the case definitions assessed performed better in patients with comorbidities than in those without. The performance of symptoms varied by age groups, with fever being of high value in older people, and cough being of high value in children. In patients with comorbidities, the performance of fever was the highest (aDOR 5.45; 95% CI 3.43–8.66). No differences in the performance of the case definition or symptoms in influenza cases according to virus type were found.

Factors associated with influenza-like-illness: a crowdsourced cohort study from 2012/13 to 2017/18

Background

Influenza generates a significant societal impact on morbidity, mortality, and associated costs. The study objective was to identify factors associated with influenza-like-illness (ILI) episodes during seasonal influenza epidemics among the general population.

Methods

A prospective study was conducted with the GrippeNet.fr crowdsourced cohort between 2012/13 and 2017/18. After having completed a yearly profile survey detailing socio-demographic, lifestyle and health characteristics, participants reported weekly data on symptoms. Factors associated with at least one ILI episode per influenza epidemic, using the European Centre for Disease Prevention and Control case definition, were analyzed through a conditional logistic regression model.

Results

From 2012/13 to 2017/18, 6992 individuals participated at least once, and 61% of them were women (n = 4258). From 11% (n = 469/4140 in 2013/14) to 29% (n = 866/2943 in 2012/13) of individuals experienced at least one ILI during an influenza epidemic. Factors associated with higher risk for ILI were: gender female (OR = 1.29, 95%CI [1.20; 1.40]), young age (< 5 years old: 3.12 [2.05; 4.68]); from 5 to 14 years old: 1.53 [1.17; 2.00]), respiratory allergies (1.27 [1.18; 1.37]), receiving a treatment for chronic disease (1.20 [1.09; 1.32]), being overweight (1.18 [1.08; 1.29]) or obese (1.28 [1.14; 1.44]), using public transport (1.17 [1.07; 1.29]) and having contact with pets (1.18 [1.09; 1.27]). Older age (≥ 75 years old: 0.70 [0.56; 0.87]) and being vaccinated against influenza (0.91 [0.84; 0.99]) were found to be protective factors for ILI.

Conclusions

This ILI risk factors analysis confirms and further completes the list of factors observed through traditional surveillance systems. It indicates that crowdsourced cohorts are effective to study ILI determinants at the population level. These findings could be used to adapt influenza prevention messages at the population level to reduce the spread of the disease.

Electronic supplementary material

The online version of this article (10.1186/s12889-019-7174-6) contains supplementary material, which is available to authorized users.

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

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.

Influenza epidemics observed in primary care from 1984 to 2017 in France: A decrease in epidemic size over time

BACKGROUND

Epidemiological analysis of past influenza epidemics remains essential to understand the evolution of the disease and optimize control and prevention strategies. Here, we aimed to use data collected by a primary care surveillance system over the last three decades to study trends in influenza epidemics and describe epidemic profiles according to circulating influenza viruses.

METHODS

Influenza-like illness (ILI) weekly incidences were estimated using cases reported by general practitioners participating in the French Sentinelles network, between 1984 and 2017. Influenza epidemics were detected by applying a periodic regression to this time series. Epidemic (co-)dominant influenza virus (sub)types were determined using French virology data.

RESULTS

During the study period, 297 607 ILI cases were reported allowing the detection of 33 influenza epidemics. On average, seasonal epidemics lasted 9 weeks and affected 4.1% of the population (95% CI 3.5; 4.7). Mean age of cases was 29 years. Epidemic size decreased over time by -66 cases per 100 000 population per season on average (95% CI -132; -0.2, P value = 0.049) and epidemic height decreased by -15 cases per 100 000 (95% CI -28; -2, P value = 0.022). Epidemic duration appeared stable over time. Epidemics were mostly dominated by A(H3N2) (n = 17, 52%), associated with larger epidemic size, higher epidemic peak and older age of cases.

CONCLUSIONS

The declining trend in influenza epidemic size and height over the last 33 years might be related to several factors like increased vaccine coverage, hygiene improvements or changing in influenza viruses. However, further researches are needed to assess the impact of potential contributing factors to adapt influenza plans.