Correlation between flu and Wikipedia’s pages visualization

Main Article Content

Vincenza Gianfredi
Omar Enzo Santangelo
Sandro Provenzano


Flu, Italy, Medical Informatics Computing, Vaccine-preventable diseases, Medical Informatics


Introduction: This study aimed to assess if the frequency of the Italian general public searches for influenza, using the Wikipedia web-page, are aligned with Istituto Superiore di Sanità (ISS) influenza cases.
Materials and Methods: The reported cases of flu were selected from October 2015 to May 2019. 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 basis. The following data were extracted: number of weekly views by users from the October 2015 to May 2019 of the pages: Influenza, Febbre and Tosse (Flu, Fever and Cough, in English). Cross-correlation results are obtained as product-moment correlations between the two times series.
Results: Regarding the database with weekly data, temporal correlation was observed between the bulletin of ISS and Wikipedia search trends. The strongest correlation was at a lag of 0 for number of cases and Flu (r=0.7571), Fever and Cough (r=0.7501). The strongest correlation was at a lag of -1 for Fever and Cough (r=0.7501). The strongest correlation was at a lag of 1 for number of cases and Flu (r=0.7559), Fever and Cough (r=0.7501).
Conclusions: A possible future application for programming and management interventions of Public Health is proposed.


Download data is not yet available.
Abstract 214 | PDF Downloads 126


1. World Health Organization. Influenza – estimating burden of disease 2020 [Available from:
2. Epicentro. Influenza, aspetti epidemiologici in Italia 2020 [Available from:
3. Rizzo C, Bella A, Viboud C, Simonsen L, Miller MA, Rota MC, et al. Trends for influenza-related deaths during pandemic and epidemic seasons, Italy, 1969-2001. Emerg Infect Dis. 2007;13(5):694-9.
4. Alagna E, Santangelo OE, Raia DD, Gianfredi V, Provenzano S, Firenze A. Health status, diseases and vaccinations of the homeless in the city of Palermo, Italy. Ann Ig. 2019;31(1):21-34.
5. Gianfredi V, Nucci D, Salvatori T, Orlacchio F, Villarini M, Moretti M, et al. "PErCEIVE in Umbria": evaluation of anti-influenza vaccination's perception among Umbrian pharmacists. J Prev Med Hyg. 2018;59(1):E14-E9.
6. Rossi D, Croci R, Affanni P, Odone A, Signorelli C. Influenza vaccination coverage in Lombardy Region: a twenty-year trend analysis (1999-2019). Acta Biomed. 2020;91(3-S):141-5.
7. Odone A, Chiesa V, Ciorba V, Cella P, Pasquarella C, Signorelli C. Influenza and immunization: a quantitative study of media coverage in the season of the <>. Epidemiol Prev. 2015;39(4 Suppl 1):139-45.
8. Signorelli C, Odone A, Miduri A, Cella P, Pasquarella C, Gozzini A, et al. Flu vaccination in elite athletes: A survey among Serie A soccer teams. Acta Biomed. 2016;87(2):117-20.
9. Gianfredi V, Moretti M, Fusco Moffa I. Burden of measles using disability-adjusted life years, Umbria 2013-2018. Acta Biomed. 2020;91(3-S):48-54.
10. Mahroum N, Bragazzi NL, Sharif K, Gianfredi V, Nucci D, Rosselli R, et al. Leveraging Google Trends, Twitter, and Wikipedia to Investigate the Impact of a Celebrity's Death From Rheumatoid Arthritis. J Clin Rheumatol. 2018;24(4):188-92.
11. Provenzano S, Santangelo OE, Giordano D, Alagna E, Piazza D, Genovese D, et al. Predicting disease outbreaks: evaluating measles infection with Wikipedia Trends. Recenti Prog Med. 2019;110(6):292-6.
12. Gianfredi V, Bragazzi NL, Nucci D, Martini M, Rosselli R, Minelli L, et al. Harnessing Big Data for Communicable Tropical and Sub-Tropical Disorders: Implications From a Systematic Review of the Literature. Front Public Health. 2018;6:90.
13. Bragazzi NL, Barberis I, Rosselli R, Gianfredi V, Nucci D, Moretti M, et al. How often people google for vaccination: Qualitative and quantitative insights from a systematic search of the web-based activities using Google Trends. Hum Vaccin Immunother. 2017;13(2):464-9.
14. Gianfredi V, Bragazzi NL, Mahamid M, Bisharat B, Mahroum N, Amital H, et al. Monitoring public interest toward pertussis outbreaks: an extensive Google Trends-based analysis. Public Health. 2018;165:9-15.
15. Santangelo OE, Provenzano S, Piazza D, Giordano D, Calamusa G, Firenze A. Digital epidemiology: assessment of measles infection through Google Trends mechanism in Italy. Ann Ig. 2019;31(4):385-91.
16. Priedhorsky R, Daughton AR, Barnard M, O'Connell F, Osthus D. Estimating influenza incidence using search query deceptiveness and generalized ridge regression. PLoS Comput Biol. 2019;15(10):e1007165.
17. Istituto Superiore di Sanità. Sistema di Sorveglianza Integrata dell’Influenza 2020 [Available from:
18. Wikipedia. Analisi di visualizzazioni delle pagine 2020 [Available from:
19. Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24(3):69-71.
20. StataCorp. Stata Statistical Software. In: Station C, editor.: StataCorp LP; 2015.
21. Hickmann KS, Fairchild G, Priedhorsky R, Generous N, Hyman JM, Deshpande A, et al. Forecasting the 2013-2014 influenza season using Wikipedia. PLoS Comput Biol. 2015;11(5):e1004239.
22. McIver DJ, Brownstein JS. Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time. PLoS Comput Biol. 2014;10(4):e1003581.
23. Istituto Nazionale di Statistica. Internet@Italia. Domanda e offerta di servizi online e scenari di digitalizzazione. Rome; 2018.
24. Istituto Nazionale di Statistica. Popolazione e famiglie 2020 [Available from:
25. Bidmon S, Terlutter R. Gender Differences in Searching for Health Information on the Internet and the Virtual Patient-Physician Relationship in Germany: Exploratory Results on How Men and Women Differ and Why. J Med Internet Res. 2015;17(6):e156.
26. Gabarron E, Lau AY, Wynn R. Is There a Weekly Pattern for Health Searches on Wikipedia and Is the Pattern Unique to Health Topics? J Med Internet Res. 2015;17(12):e286.
27. Smith DA. Situating Wikipedia as a health information resource in various contexts: A scoping review. PLoS One. 2020;15(2):e0228786.
28. Generous N, Fairchild G, Deshpande A, Del Valle SY, Priedhorsky R. Global disease monitoring and forecasting with Wikipedia. PLoS Comput Biol. 2014;10(11):e1003892.
29. Sharpe JD, Hopkins RS, Cook RL, Striley CW. Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis. JMIR Public Health Surveill. 2016;2(2):e161.
30. Bragazzi NL, Gianfredi V, Villarini M, Rosselli R, Nasr A, Hussein A, et al. Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is ("Isolate-Inactivate-Inject") Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview. Front Public Health. 2018;6:62.
31. Gianfredi V, Odone A, Fiacchini D, Rosselli R, Battista T, Signorelli C. Trust and reputation management, branding, social media management nelle organizzazioni sanitarie: sfide e opportunità per la comunità igienistica italiana. J Prev Med Hyg. 2019;60(3):E108-E9.
32. Gianfredi V, Grisci C, Nucci D, Parisi V, Moretti M. [Communication in health.]. Recenti Prog Med. 2018;109(7):374-83.
33. Gianfredi V, Balzarini F, Gola M, Mangano S, Carpagnano LF, Colucci ME, et al. Leadership in Public Health: Opportunities for Young Generations Within Scientific Associations and the Experience of the "Academy of Young Leaders". Front Public Health. 2019;7:378.

Most read articles by the same author(s)

1 2 > >>