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. 2017 Jan 11;12(1):e0169801.
doi: 10.1371/journal.pone.0169801. eCollection 2017.

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

Daniela Perrotta  1 , Antonino Bella  2 , Caterina Rizzo  2 , Daniela Paolotti  1
Affiliations

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

Daniela Perrotta et al. PLoS One. .

Abstract

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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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Geographic distribution of the Influweb active participants, the Influnet sample and the Italian population.
The color code indicates the proportion of the population living in the Italian regions. The maps were created by manipulating publicly available shapefile at the region level.
Fig 2
Fig 2. Influweb age and gender distributions.
Age and gender distributions of the Influweb active participants in comparison with the Italian population for the 2014–2015 influenza season.
Fig 3
Fig 3. Influweb and Influnet vaccination rate for 65+ years old.
Fig 4
Fig 4. Incidence rates among the Influweb active participants in comparison with the Influnet incidence curve.
For ease of comparison, the curves are reported on different scales.
Fig 5
Fig 5. Cross-correlation between the smoothed time series for Influweb data and the Influnet curve.
(A), (C), (E) show the incidence curves for Influnet and the smoothed one for Influweb. (B), (D), (F) show the cross-correlation as a function of the lag (weeks) between the two time series.
Fig 6
Fig 6. Health-care seeking behavior.
Proportion of volunteers reporting ILI symptoms who sought healthcare assistance or consultation during the three influenza seasons under study.
Fig 7
Fig 7. Age distribution of the proportion of participants seeking medical service during ILI episode.
Fig 8
Fig 8. Age distribution of the proportion of participants who changed their daily routine during ILI episode.
See this image and copyright information in PMC

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