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Data Brief. 2016 Nov 2;9:839-845. eCollection 2016 Dec.

Infodemiological data of West-Nile virus disease in Italy in the study period 2004-2015.

Author information

1
School of Public Health, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy; UNESCO CHAIR "Anthropology of Health - Biosphere and Healing System", University of Genoa, Genoa, Italy; Department of Mathematics (DIMA), University of Genoa, Genoa, Italy; Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy.
2
Galliera Hospital, Department of Neurosurgery, Genoa, Italy.
3
Neurosciences Critical Care Unit, Addenbrooke׳s Hospital, Cambridge University, Cambridge University Hospitals Trust, Cambridge, United Kingdom.
4
UNESCO CHAIR "Anthropology of Health - Biosphere and Healing System", University of Genoa, Genoa, Italy; Department of Mathematics (DIMA), University of Genoa, Genoa, Italy.
5
Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, Section of Psychiatry, University of Genoa, Genoa, Italy.
6
Department of Neurosciences, Biomedical, and Movement Sciences, University of Verona, Italy; Department of Neurology, Franz Tappeiner Hospital, Merano, Italy.

Abstract

Google Trends (GT) was mined from 2004 to 2015, searching for West-Nile virus disease (WNVD) in Italy. GT-generated data were modeled as a time series and were analyzed using classical time series analyses. In particular, correlation between GT-based Relative Search Volumes (RSVs) related to WNVD and "real-world" epidemiological cases in the same study period resulted r=0.76 (p<0.0001) on a monthly basis and r=0.80 (p<0.0001) on a yearly basis. The partial autocorrelation analysis and the spectral analysis confirmed that a 1-year regular pattern could be detected. Correlation between GT-based RSVs related to WNVD yielded a r=0.54 (p<0.05) on a regional basis. Summarizing, GT-generated data concerning WNVD well correlated with epidemiology and could be exploited for complementing traditional surveillance.

KEYWORDS:

Google Trends; Infodemiology and infoveillance; West-Nile virus disease

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