Ten-year performance of Influenzanet: ILI time series, risks, vaccine effects, and care-seeking behaviour

Noort, S P van; Codeço, Cláudia Torres; Koppeschaar, Carl; Ranst, Marc Van; Paolotti, Daniela; Gomes, M. Gabriela M.

doi:10.1016/j.epidem.2015.05.001

Cited by 58 publications

(78 citation statements)

References 43 publications

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“…This is in line with what has been observed previously with web-based platforms for influenza surveillance in other countries [17, 18]. Moreover, as shown in the panels B, D and F in Fig 5, Influweb detected the peak incidence one week earlier than sentinel doctors surveillance, thus suggesting that Influweb may be able to detect temporal variations in incidence rates in advance with respect to the sentinel doctors surveillance.…”

Section: Discussionsupporting

confidence: 90%

Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy

et al. 2017

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The monitoring of seasonal influenza yearly epidemics remains one of the main activity of national syndromic surveillance systems. The development of internet-based surveillance tools has brought an innovative approach to seasonal influenza surveillance by directly involving self-selected volunteers among the general population reporting their health status on a weekly basis throughout the flu season. In this paper, we explore how Influweb, an internet-based monitoring system for influenza surveillance, deployed in Italy since 2008 has performed during three years from 2012 to 2015 in comparison with data collected during the same period by the Italian sentinel doctors surveillance system.

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Section: Discussionsupporting

confidence: 90%

Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy

et al. 2017

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“…Crowdsourced Internet-based participatory syndromic surveillance programs, such as Influenzanet in Europe, FluTracking in Australia, and Flu Near You (FNY) in the United States and Canada, have also been developed to track community influenza activity. These systems correlate well with traditional, clinical-based influenza-like illness (ILI) activity surveillance tools [14-17], and other platforms, such as GoViral, have validated the use of participatory information for disease surveillance by comparing volunteers’ self-reported symptoms with specimens [18]. Although participatory surveillance systems track flu activity in a timely fashion, a large, diverse cohort of users who participate regularly and are representative of the population is essential for these systems to work effectively.…”

Section: Introductionmentioning

confidence: 98%

Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System

Baltrusaitis¹,

Santillana²,

Crawley³

et al. 2017

JMIR Public Health Surveill

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BackgroundFlu Near You (FNY) is an Internet-based participatory surveillance system in the United States and Canada that allows volunteers to report influenza-like symptoms using a brief weekly symptom report.ObjectiveOur objective was to evaluate the representativeness of the FNY population compared with the general population of the United States, explore the demographic and behavioral characteristics associated with FNY’s high-participation users, and summarize results from a user survey of a cohort of FNY participants.MethodsWe compared (1) the representativeness of sex and age groups of FNY participants during the 2014-2015 flu season versus the general US population and (2) the distribution of Human Development Index (HDI) scores of FNY participants versus that of the general US population. We analyzed associations between demographic and behavioral factors and the level of participant follow-up (ie, high vs low). Finally, descriptive statistics of responses from FNY’s 2015 and 2016 end-of-season user surveys were calculated.ResultsDuring the 2014-2015 influenza season, 47,234 unique participants had at least one FNY symptom report that was either self-reported (users) or submitted on their behalf (household members). The proportion of female FNY participants was significantly higher than that of the general US population (n=28,906, 61.2% vs 51.1%, P<.001). Although each age group was represented in the FNY population, the age distribution was significantly different from that of the US population (P<.001). Compared with the US population, FNY had a greater proportion of individuals with HDI >5.0, signaling that the FNY user distribution was more affluent and educated than the US population baseline. We found that high-participation use (ie, higher participation in follow-up symptom reports) was associated with sex (females were 25% less likely than men to be high-participation users), higher HDI, not reporting an influenza-like illness at the first symptom report, older age, and reporting for household members (all differences between high- and low-participation users P<.001). Approximately 10% of FNY users completed an additional survey at the end of the flu season that assessed detailed user characteristics (3217/33,324 in 2015; 4850/44,313 in 2016). Of these users, most identified as being either retired or employed in the health, education, and social services sectors and indicated that they achieved a bachelor’s degree or higher.ConclusionsThe representativeness of the FNY population and characteristics of its high-participation users are consistent with what has been observed in other Internet-based influenza surveillance systems. With targeted recruitment of underrepresented populations, FNY may improve as a complementary system to timely tracking of flu activity, especially in populations that do not seek medical attention and in areas with poor official surveillance data.

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“…For example, some researchers have demonstrated how members of the general public can be recruited to receive messages by email or text routinely asking them to report the onset of symptoms, such as influenza-like illness (80,87). Surveillance systems based on participatory cohorts provide information similar to that obtained through sentinel surveillance (4), and they address some of the limitations of surveillance methods based on telephone survey, which may not adequately sample populations without telephone lines (44,55).…”

Section: The Role Of Informatics In Surveillance Systemsmentioning

confidence: 99%

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

Groseclose

Buckeridge

2017

Annu. Rev. Public Health

237

192

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Surveillance is critical for improving population health. Public health surveillance systems generate information that drives action, and the data must be of sufficient quality and with a resolution and timeliness that matches objectives. In the context of scientific advances in public health surveillance, changing health care and public health environments, and rapidly evolving technologies, the aim of this article is to review public health surveillance systems. We consider their current use to increase the efficiency and effectiveness of the public health system, the role of system stakeholders, the analysis and interpretation of surveillance data, approaches to system monitoring and evaluation, and opportunities for future advances in terms of increased scientific rigor, outcomes-focused research, and health informatics.

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Ten-year performance of Influenzanet: ILI time series, risks, vaccine effects, and care-seeking behaviour

Cited by 58 publications

References 43 publications

Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy

Participatory Online Surveillance as a Supplementary Tool to Sentinel Doctors for Influenza-Like Illness Surveillance in Italy

Determinants of Participants’ Follow-Up and Characterization of Representativeness in Flu Near You, A Participatory Disease Surveillance System

Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation

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