ArticleInfectious diseases2020
Can Users Search Trends Predict People Scares or Disease Breakout? An Examination of Infectious Skin Diseases in the United States.
Article in Infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- Assessing Public Interest in Mpox via Google Trends, YouTube, and TikTok.JMIR dermatology · 2023Article
- Article
- Worldwide Evolution of Vaccinable and Nonvaccinable Viral Skin Infections: Google Trends Analysis.JMIR dermatology · 2022Article
- Epidemiologic evolution of common cutaneous infestations and arthropod bites: A Google Trends analysis.JAAD international · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn health and medicine, people heavily use the Internet to search for information about symptoms, diseases, and treatments. As such, the Internet information can simulate expert medical doctors, pharmacists, and other health care providers.
aimThis article aims to evaluate a dataset of search terms to determine whether search queries and terms can be used to reliably predict skin disease breakouts. Furthermore, the authors propose and evaluate a model to decide when to declare a particular month as Epidemic at the US national level.
methodsA Model was designed to distinguish a breakout in skin diseases based on the number of monthly discovered cases. To apply this model, the authors correlated Google Trends of popular search terms with monthly reported Rubella and Measles cases from Centers for Disease Control and Prevention (CDC). Regressions and decision trees were used to determine the impact of different terms to trigger the occurrence of epidemic classes.
resultsResults showed that the volume of search keywords for Rubella and Measles rises when the volume of those reported diseases rises. Results also implied that the overall process was successful and should be repeated with other diseases. Such process can trigger different actions or activities to be taken when a certain month is declared as "Epidemic." Furthermore, this research has shown great interest for vaccination against Measles and Rubella.
conclusionsThe findings suggest that the search queries and keyword trends can be truly reliable to be used for the prediction of disease outbreaks and some other related knowledge extraction applications. Also search-term surveillance can provide an additional tool for infectious disease surveillance. Future research needs to re-apply the model used in this article, and researchers need to question whether characterizing the epidemiology of Coronavirus Disease 2019 (COVID-19) pandemic waves in United States can be done through search queries and keyword trends.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.