Digital epidemiology and infodemiology of hand-foot-mouth disease (HFMD) in Italy. Disease trend assessment via Google and Wikipedia

Main Article Content

Omar Enzo Santangelo
Vincenza Gianfredi
Sandro Provenzano
Fabrizio Cedrone


Hand-foot-mouth disease, HFMD, Italy, Digital Epidemiology, Google, Wikipedia, Epidemiology, Infectious Diseases, Infodemiology, Medical Informatics Computing


Background and aim: The study aimed to evaluate the epidemiological trend of hand, foot and mouth disease (HFMD) in Italy using data on Internet search volume.

Methods: A cross-sectional study design was used. Data on Internet searches were obtained from Google Trends (GT) and Wikipedia. We used the following Italian search term: “Malattia mano-piede-bocca” (Hand-foot-mouth disease, in English). A monthly time-frame was extracted, partly overlapping, from July 2015 to December 2022. GT and Wikipedia were overlapped to perform a linear regression and correlation analyses. Statistical analyses were performed using the Spearman's rank correlation coefficient (rho). A linear regression analysis was performed considering Wikipedia and GT.

Results: Search peaks for both Wikipedia and GT occurred in the months November-December during the autumn-winter season and in June during the spring-summer season, except for the period from June 2020 to June 2021, probably due to the restrictions of the COVID19 pandemic. A temporal correlation was observed between GT and Wikipedia search trends.

Conclusions: This is the first study in Italy that attempts to clarify the epidemiology of HFMD. Google search and Wikipedia can be valuable for public health surveillance; however, to date, digital epidemiology cannot replace the traditional surveillance system.


Download data is not yet available.
Abstract 119 | PDF Downloads 92


1. Esposito S, Principi N. Hand, foot and mouth disease: current knowledge on clinical manifestations, epidemiology, aetiology and prevention. Eur J Clin Microbiol Infect Dis. 2018;37(3):391-398. doi:10.1007/s10096-018-3206-x
2. CDC, Center for Diseases Control and Prevention. USA. Available online: (last access January 09, 2022)
3. Solomon T, Lewthwaite P, Perera D, Cardosa MJ, McMinn P, Ooi MH. Virology, epidemiology, pathogenesis, and control of enterovirus 71. Lancet Infect Dis. 2010;10(11):778-790. doi:10.1016/S1473-3099(10)70194-8
4. Guerra AM, Orille E, Waseem M. Hand, Foot, and Mouth Disease. In: StatPearls. Treasure Island (FL): StatPearls Publishing; October 9, 2022.
5. Huang J, Liao Q, Ooi MH, et al. Epidemiology of Recurrent Hand, Foot and Mouth Disease, China, 2008-2015. Emerg Infect Dis. 2018;24(3):432-442. doi:10.3201/eid2403.171303
6. Ruan F, Yang T, Ma H, et al. Risk factors for hand, foot, and mouth disease and herpangina and the preventive effect of hand-washing. Pediatrics. 2011;127(4):e898-e904. doi:10.1542/peds.2010-1497
7. Wang YR, Sun LL, Xiao WL, Chen LY, Wang XF, Pan DM. Epidemiology and clinical characteristics of hand foot, and mouth disease in a Shenzhen sentinel hospital from 2009 to 2011. BMC Infect Dis. 2013;13:539. doi:10.1186/1471-2334-13-539
8. Nguyen NT, Pham HV, Hoang CQ, et al. Epidemiological and clinical characteristics of children who died from hand, foot and mouth disease in Vietnam, 2011. BMC Infect Dis. 2014;14:341. doi:10.1186/1471-2334-14-341
9. Abedi GR, Watson JT, Nix WA, Oberste MS, Gerber SI. Enterovirus and Parechovirus Surveillance - United States, 2014-2016. MMWR Morb Mortal Wkly Rep. 2018;67(18):515-518. doi:10.15585/mmwr.mm6718a2
10. Santangelo OE, Gianfredi V, Provenzano S. Wikipedia searches and the epidemiology of infectious diseases: A systematic review. Data & Knowledge Engineering. 2022; 142: 102093. doi: 10.1016/j.datak.2022.102093
11. Provenzano S, Gianfredi V, Santangelo OE. Insight the data: Wikipedia's researches and real cases of arboviruses in Italy. Public Health. 2021;192:21-29. doi:10.1016/j.puhe.2020.12.010
12. Provenzano S, Santangelo OE, Giordano D, et al. Predicting disease outbreaks: evaluating measles infection with Wikipedia Trends. Recenti Prog Med. 2019;110(6):292-296. doi:10.1701/3182.31610
13. Gianfredi V, Santangelo OE, Provenzano S. Correlation between flu and Wikipedia's pages visualization. Acta Biomed. 2021;92(1):e2021056. doi:10.23750/abm.v92i1.9790
14. Santangelo OE, Provenzano S, Gianfredi V. Infodemiology of flu: Google trends-based analysis of Italians' digital behavior and a focus on SARS-CoV-2, Italy. J Prev Med Hyg. 2021;62(3):E586-E591. doi:10.15167/2421-4248/jpmh2021.62.3.1704
15. Santangelo OE, Provenzano S, Grigis D, Giordano D, Armetta F, Firenze A. Can Google Trends and Wikipedia help traditional surveillance? A pilot study on Measles. Acta Biomed. 2020; 91 (4):e2020190. doi: 10.23750/abm.v91i4.8888
16. 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):385-391. doi:10.7416/ai.2019.2300
17. Riccò M, Baldassarre A, Provenzano S, et al. Infodemiology of RSV in Italy (2017-2022): An Alternative Option for the Surveillance of Incident Cases in Pediatric Age?. Children (Basel). 2022;9(12):1984. doi:10.3390/children9121984
18. Google Trends. Available online: (last access January 9, 2022)
19. Wikipedia. Available from: (last accessed January 9, 2022)
20. Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J. 2012;24(3):69-71.
21. White KJ. The Durbin-Watson Test for Autocorrelation in Nonlinear Models 370 The Review of Economics and Statistics the Durbin-Watson Test for Autocorrelation in Nonlinear Models. Rev Econ Stat. 1992; 74:370–373.
22. StataCorp. Stata Statistical Software. In: Station C, editor.: StataCorp LP; 2015.
23. Niu Q, Liu J, Zhao Z, et al. Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study. BMC Infect Dis. 2022;22(1):806. doi:10.1186/s12879-022-07790-9
24. Kow RY, Mohamad Rafiai N, Ahmad Alwi AA, et al. COVID-19 Infodemiology: Association Between Google Search and Vaccination in Malaysian Population. Cureus. 2022;14(9):e29515. doi:10.7759/cureus.29515
25. Springer S, Zieger M, Strzelecki A. The rise of infodemiology and infoveillance during COVID-19 crisis. One Health. 2021;13:100288. doi:10.1016/j.onehlt.2021.100288
26. Nucci D, Santangelo OE, Nardi M, Provenzano S, Gianfredi V. Wikipedia, Google Trends and Diet: Assessment of Temporal Trends in the Internet Users' Searches in Italy before and during COVID-19 Pandemic. Nutrients. 2021;13(11):3683. doi:10.3390/nu13113683
27. Cai O, Sousa-Pinto B. United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study. JMIR Public Health Surveill. 2022;8(3):e32364. doi:10.2196/32364
28. Santangelo OE, Gentile V, Pizzo S, Giordano D, Cedrone F. Machine Learning and Prediction of Infectious Diseases: A Systematic Review. Machine Learning and Knowledge Extraction. 2023; 5(1):175-198. doi:10.3390/make5010013
29. Gianfredi V, Santangelo OE, Provenzano S. The effects of COVID-19 pandemic on the trend of measles and influenza in Europe. Acta Biomed. 2021;92(4):e2021318. doi:10.23750/abm.v92i4.11558
30. Gianfredi V, Bragazzi NL, Mahamid M, et al. Monitoring public interest toward pertussis outbreaks: an extensive Google Trends-based analysis. Public Health. 2018;165:9-15. doi:10.1016/j.puhe.2018.09.001
31. Riccò M, Valente M, Marchesi F. Are symptoms associated with SARS-CoV-2 infections evolving over time?. Infect Dis Now. 2022;52(2):110-112. doi:10.1016/j.idnow.2022.01.006
32. Salathé M. Digital epidemiology: what is it, and where is it going?. Life Sci Soc Policy. 2018;14(1):1. doi:10.1186/s40504-017-0065-7
33. Malik S. A Comparative Study of two major Search Engines: Google and Yahoo . Orient J Comp Sci and Technol. 7(1)

Most read articles by the same author(s)

1 2 > >>