Infodemiology and infoveillance: framework for contagious exanthematous diseases, of childhood in Italy.

Italy Sixth disease digital epidemiology fifth disease fourth disease infodemiology infoveillance scarlet fever

Journal

Pathogens and global health
ISSN: 2047-7732
Titre abrégé: Pathog Glob Health
Pays: England
ID NLM: 101583421

Informations de publication

Date de publication:
27 Feb 2024
Historique:
medline: 27 2 2024
pubmed: 27 2 2024
entrez: 27 2 2024
Statut: aheadofprint

Résumé

Contagious exanthematous diseases are becoming a major public health problem. The purpose of this study was to evaluate the potential epidemiological trend of four infectious exanthematous diseases in Italy through the searches on the internet. We used the following Italian search term: 'Sesta malattia' (Sixth Disease, in English), 'Eritema Infettivo' (also knows 'Quinta malattia' in Italian; Fifth Disease in English), 'Quarta malattia' (Fourth Disease in English) and 'Scarlattina' (Scarlet fever in English). We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using the Spearman's rank correlation coefficient (rho). The study period is between July 2015 and December 2022. The diseases considered have a seasonal trend and the search peaks between GT and Wikipedia overlap. A temporal correlation was observed between GT and Wikipedia search trends. Google Trends Internet search data showed strong correlation with Wikipedia with a rho statistically significant for Fifth disease (rho = 0.78), Fourth disease (rho = 0.76) and Scarlet-fever (rho = 0.77), moderate correlation for Sixth disease (rho = 0.32). Infectious disease searches using Google and Wikipedia can be useful for public health surveillance and help policy makers implement prevention and information programs for the population, in addition to the fact that increases in searches could represent an early warning in the detection of outbreaks.

Sections du résumé

BACKGROUND UNASSIGNED
Contagious exanthematous diseases are becoming a major public health problem. The purpose of this study was to evaluate the potential epidemiological trend of four infectious exanthematous diseases in Italy through the searches on the internet.
METHODS UNASSIGNED
We used the following Italian search term: 'Sesta malattia' (Sixth Disease, in English), 'Eritema Infettivo' (also knows 'Quinta malattia' in Italian; Fifth Disease in English), 'Quarta malattia' (Fourth Disease in English) and 'Scarlattina' (Scarlet fever in English). We overlapped Google Trends and Wikipedia data to perform a linear regression and correlation analysis. Statistical analyses were performed using the Spearman's rank correlation coefficient (rho). The study period is between July 2015 and December 2022.
RESULTS UNASSIGNED
The diseases considered have a seasonal trend and the search peaks between GT and Wikipedia overlap. A temporal correlation was observed between GT and Wikipedia search trends. Google Trends Internet search data showed strong correlation with Wikipedia with a rho statistically significant for Fifth disease (rho = 0.78), Fourth disease (rho = 0.76) and Scarlet-fever (rho = 0.77), moderate correlation for Sixth disease (rho = 0.32).
CONCLUSIONS UNASSIGNED
Infectious disease searches using Google and Wikipedia can be useful for public health surveillance and help policy makers implement prevention and information programs for the population, in addition to the fact that increases in searches could represent an early warning in the detection of outbreaks.

Identifiants

pubmed: 38411130
doi: 10.1080/20477724.2024.2323844
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-8

Auteurs

Sandro Provenzano (S)

Local Health Unit of Trapani, ASP Trapani, Trapani, Italy.

Omar Enzo Santangelo (OE)

CS Vaccinations and Infectious Disease Surveillance, Regional Health Care and Social Agency of Lodi, Lodi, Italy.

Vincenza Gianfredi (V)

Department of Biomedical Sciences for Health, University of Milan, Milan, Italy.

Classifications MeSH