TY  -  JOUR
AU  -  Provenzano, Sandro
AU  -  Santangelo, Omar Enzo
AU  -  Giordano, Domiziana
AU  -  Alagna, Enrico
AU  -  Piazza, Dario
AU  -  Genovese, Dario
AU  -  Calamusa, Giuseppe
AU  -  Firenze, Alberto
T1  -  Predicting disease outbreaks: 
evaluating measles infection with Wikipedia Trends
PY  -  2019
Y1  -  2019-06-01
DO  -  10.1701/3182.31610
JO  -  Recenti Progressi in Medicina
JA  -  Recenti Prog Med
VL  -  110
IS  -  6
SP  -  292
EP  -  296
PB  -  Il Pensiero Scientifico Editore
SN  -  2038-1840
Y2  -  2026/05/06
UR  -  http://dx.doi.org/10.1701/3182.31610
N2  -  Summary. The primary aim of this study was to evaluate the temporal correlation between Wikitrends and conventional surveillance data generated for measles infection reported by bulletin of Istituto Superiore di Sanità (ISS). The reported cases of measles were selected from July 2015 to October 2018. Wikipedia Trends was used to assess how many times a specific page was read by users, data were extracted as daily data and aggregated on a weekly and monthly basis. The following data were extracted: number of views by users from 1 July 2015 to 31 October 2018 of the Morbillo, Vaccinazione del Morbillo, Vaccinazione MPR and Macchie di Koplik pages (Measles, Measles Vaccination, MPR Vaccination and Koplik’s spots in English). Cross-correlation results were obtained as product-moment correlations between the two time series. Regarding the database with monthly data, temporal correlation was observed between the bulletin of ISS and Wikipedia search trends: the strongest correlation was at a lag of 0 for Measles (r=0.9164), Measles Vaccination (r=0.8622), MPR Vaccination (r=0.7852) and Koplik’s spots (r=0.8217). Regarding the database with weekly data, both moderate and strong correlation was observed. A possible future application for programming and management interventions of Public Health is proposed.
ER  -