Evaluating the Feasibility and Participants’ Representativeness of an Online Nationwide Surveillance System for Influenza in France

The increasing Internet coverage and the widespread use of digital devices offer the possibility to develop new digital surveillance systems potentially capable to provide important aid to epidemiological and public health monitoring and research. In France, a new nationwide surveillance system for influenza-like illness, GrippeNet.fr, was introduced since the 2011/2012 season based on an online participatory mechanism and open to the general population. We evaluate the recruitment and participation of users to the first pilot season with respect to similar efforts in Europe to assess the feasibility of establishing a participative network of surveillance in France. We further investigate the representativeness of the GrippeNet.fr population along a set of indicators on geographical, demographic, socio-economic and health aspects. Participation was widespread in the country and with rates comparable to other European countries with partnered projects running since a longer time. It was not representative of the general population in terms of age and gender, however all age classes were represented, including the older classes (65+ years old), generally less familiar with the digital world, but considered at high risk for influenza complications. Once adjusted on demographic indicators, the GrippeNet.fr population is found to be more frequently employed, with a higher education level and vaccination rate with respect to the general population. A similar propensity to commute for work to different regions was observed, and no significant difference was found for asthma and diabetes. Results show the feasibility of the system, provide indications to inform adjusted epidemic analyses, and highlight the presence of specific population groups that need to be addressed by targeted communication strategies to achieve a higher representativeness in the following seasons.


GrippeNet.fr intake survey (English translation of the original survey).
Mandatory questions are followed by a *. Where the options is "pick only one" (e.g. Intake Q1) we used filled bullet points; where it is "pick all that apply" (e.g. Intake Q5) we used open bullet points.

Intake Q0
For whom are you filling this survey in?* • Myself • A member of my household • Someone else Intake Q0b I hereby certify that I have explained to this people the content and implications of this study, and obtained his free consent to participate. This people gave me the authorisation to answer to his questionnaires.* • Yes • No If « No », it is not possible to fill in the questionnaire, or to answer to other questionnaires. If « Yes », the following text appears : « Please answer to all these questions as if you were this person ».

Intake Q1
What is your gender?* • Male • Female

Intake Q2
What is your date of birth (month and year)?* If the date of birth indicates that the participant is under 18 , it is not possible to fill in the questionnaire, or to answer other questionnaires.

Intake Q3
What is your home postal code?* This information is not registered in the database, and only allows answering easily to the next question.

Intake Q3b
What is your home municipality?* Drop-down list thanks to the answer of the question Q3.

Intake Q4b (if Yes to Q4)
What is your school/college/workplace postal code (where you spend the majority of your working/studying time)?
• XXXX • I don't know/can't remember • Not applicable (e.g. don't have a fixed workplace) This information is not registered in the database, and only allows answering easily to the next question.

Intake Q4c
What is your school/college/workplace municipality? Drop-down list thanks to the answer of the question Q3.
Intake Q4d (if "Yes, Paid employment full time", or "Yes, paid employment part time" to Q4) Which of the following descriptions most closely matches with your main occupation?

Intake Q9b
What is your height (in centimeters)?

Intake Q9c
What is your weight (in kilograms)?    with the corresponding links obtained in the GrippeNet.fr sample.

Extraction of the Backbone of the commuting network
In order to extract the most relevant backbone of the census commuting network to be compared with the GrippeNet.fr network, we applied the disparity filter algorithm (1) that selects the links displaying statistically significant deviations with respect to a null model for the local assignment of weights to links. The null model used to define anomalous fluctuations is based on the null hypothesis that normalized weights are produced by a random assignment from a uniform distribution. Thus the disparity filter method selects the links, which deviate from the null hypothesis with a certain level of statistical significance α.
The choice of the level of significance varies according to the purpose of the study and the properties of the network (1). In or case values of α in the range [0.005, 0.05] were all equivalent in selecting a minimal network (i.e. 10-15% of the links) including a large proportion of the total number of commuters (60-75%). These α values also yielded equivalent results in terms of overlap between the filtered network and the GrippeNet.fr one.
In Figure 4 we show the case with α = 0.01.

Probability of observing a link in the GrippeNet.fr commuting network
The probability of a link in the census commuting network to be represented in the GrippeNet.fr network is computed as follow. Given the number of commuters OD w along a given OD direction in the census data, the probability that an individual living in O would commute to D is given by Outgoing flows per region. The color code indicates the relative difference (RD) between the regional distribution of the GrippeNet.fr flows of commuters and of the French census flows of commuters.