Predicting disease outbreaks: Evaluating measles infection with Wikipedia Trends

Giuseppe Calamusa, Omar Enzo Santangelo, Alberto Firenze, Dario Genovese, Debora Piazza, Domiziana Giordano, Sandro Provenzano

Risultato della ricerca: Articlepeer review

6 Citazioni (Scopus)

Abstract

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.
Lingua originaleEnglish
pagine (da-a)292-296
Numero di pagine5
RivistaRECENTI PROGRESSI IN MEDICINA
Volume110
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • ???subjectarea.asjc.2700.2700???

Fingerprint Entra nei temi di ricerca di 'Predicting disease outbreaks: Evaluating measles infection with Wikipedia Trends'. Insieme formano una fingerprint unica.

Cita questo