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Web-based surveillance of respiratory infection outbreaks

Retrospective analysis of Italian Covid-19 epidemic waves using Google Trends

Dados Bibliográficos

ID22078552
AutoresGloria Porcu (0000-0002-9871-4129, University of Milano-Bicocca), Yu Xi Chen (Ministero della Salute), Andrea Stella Bonaugurio (Ministero della Salute), Simone Villa (0000-0002-0753-8853, University of Milan), Leonardo Riva (University of Milano-Bicocca), Vincenzina Messina (University of Milano-Bicocca), Giorgio Bagarella (Ministero della Salute), Mauro Maistrello (Ministero della Salute), Olivia Leoni (0000-0002-6906-2424, Ministero della Salute), Danilo Cereda (0000-0001-5945-5710, Ministero della Salute), Fulvio Matone, Andrea Gori (0009-0005-6356-9266, University of Milan), Giovanni Corrao (0000-0002-1034-8444, Ministero della Salute, autor correspondente)
Ano2023
Volume11
Páginas1141688-1141688
Data de publicação2023-05-18
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoFrontiers in Public Health (JOURNAL)
Identificadores do periódicoISSN: 2296-2565 • E-ISSN: 2296-2565
EditoraFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2023.1141688
PMID37275497
OpenAlexW4377023313
IdiomaEN
Citações recebidas1
Referências citadas66

Introduction: Large-scale diagnostic testing has been proven insufficient to promptly monitor the spread of the Coronavirus disease 2019. Electronic resources may provide better insight into the early detection of epidemics. We aimed to retrospectively explore whether the Google search volume has been useful in detecting Severe Acute Respiratory Syndrome Coronavirus outbreaks early compared to the swab-based surveillance system. Methods: The Google Trends website was used by applying the research to three Italian regions (Lombardy, Marche, and Sicily), covering 16 million Italian citizens. An autoregressive-moving-average model was fitted, and residual charts were plotted to detect outliers in weekly searches of five keywords. Signals that occurred during periods labelled as free from epidemics were used to measure Positive Predictive Values and False Negative Rates in anticipating the epidemic wave occurrence. Results: Signals from "fever," "cough," and "sore throat" showed better performance than those from "loss of smell" and "loss of taste." More than 80% of true epidemic waves were detected early by the occurrence of at least an outlier signal in Lombardy, although this implies a 20% false alarm signals. Performance was poorer for Sicily and Marche. Conclusion: Monitoring the volume of Google searches can be a valuable tool for early detection of respiratory infectious disease outbreaks, particularly in areas with high access to home internet. The inclusion of web-based syndromic keywords is promising as it could facilitate the containment of COVID-19 and perhaps other unknown infectious diseases in the future

Disease · Outbreak · COVID-19 epidemiological studies · Data-Driven Disease Surveillance · Medicine · Respiratory viral infections research · Internal Medicine · Virology

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Obras citantes distintas1
Citações por ano0,5
Intervalo de citações2024 - 2024 (1)
Velocidade de citaçãorecent
Altamente citadoNão
Tipos de citaçãoNeutras: 1
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