Impact on online research on celebrities' uncommon diseases: the curious case of Justin Bieber and Ramsay Hunt syndrome

Article

Using Google Trends and Wikipedia to Investigate the Global Public’s Interest in the Pancreatic Cancer Diagnosis of a Celebrity

Vincenza Gianfredi et al.

International Journal of Environmental Research and Public Health (2023)

Review Computer Science, Artificial Intelligence

Machine Learning and Prediction of Infectious Diseases: A Systematic Review

Omar Enzo Santangelo et al.

Summary: The aim of this study is to examine the feasibility of predicting infectious disease outbreaks using machine learning. The study followed the guidelines of reputable organizations such as the Cochrane Collaboration and utilized reliable databases like PubMed/Medline and Scopus for literature search. The systematic review included 75 records and demonstrated that the combination of various machine learning techniques can accurately predict the incidence and trends of infectious diseases.

MACHINE LEARNING AND KNOWLEDGE EXTRACTION (2023)

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Review Computer Science, Artificial Intelligence

Wikipedia searches and the epidemiology of infectious diseases: A systematic review

Omar Enzo Santangelo et al.

Summary: This review examines the application of Wikipedia in epidemiologic surveillance. The results suggest that Wikipedia can serve as a scientifically valid surveillance system and fill the gaps in existing traditional surveillance systems. The objectives of studies on different infectious diseases vary, but the main goals are to understand public interest, explore organizational use of Wikipedia, and assess the accuracy of Wikipedia content. These new surveillance strategies should be implemented and can support traditional surveillance.

DATA & KNOWLEDGE ENGINEERING (2022)

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Article Public, Environmental & Occupational Health

Insight the data: Wikipedia's researches and real cases of arboviruses in Italy

Sandro Provenzano et al.

Summary: This study evaluated the temporal correlation between Wikitrends and conventional surveillance data for four virus infections in Italy, showing strong correlations for Chikungunya and West Nile Virus, and moderate correlations for Dengue and Zika virus. The integration of digital epidemiology tools with classical surveillance systems can provide real-time indicators of infectious disease spread.

PUBLIC HEALTH (2021)

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Review Public, Environmental & Occupational Health

What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review

Vincenza Gianfredi et al.

Summary: A systematic review of 19 articles found that the COVID-19 pandemic significantly impacted the mental health of the global population, resulting in increased feelings of depression, anxiety, and stress among internet users. Novel data streams can assist public health experts and policymakers in developing long-term strategies to alleviate symptoms and address mental health disorders.

PUBLIC HEALTH (2021)

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Review Health Care Sciences & Services