ArticlePloS one2018
Using search engine big data for predicting new HIV diagnoses.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Insights on the implications of COVID-19 mitigation measures for mental health.Economics and human biology · 2021Pooled it
- Multi-source machine learning for short-term influenza forecasting in Hunan Province, China: a spatiotemporal analysis using surveillance, search, meteorological and air-quality data.BMC infectious diseases · 2026Article
- The association between Google Trends suicide-related Internet searches and self-harm hospitalizations and suicide mortality in Canada.Health promotion and chronic disease prevention in Canada : research, policy and practice · 2026Article
- Trending ticks: using Google Trends data to understand tickborne disease prevention.Frontiers in public health · 2024Article
- Measles and Pertussis outbreaks in England and Wales: a time-series analysis.NIHR open research · 2024Article
- Digital Epidemiological Approaches in HIV Research: a Scoping Methodological Review.Current HIV/AIDS reports · 2023Article
- Predicting HIV infection in the decade (2005-2015) pre-COVID-19 in Zimbabwe: A supervised classification-based machine learning approach.PLOS digital health · 2023Article
- Three dimensions of COVID-19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis.Risk analysis : an official publication of the Society for Risk Analysis · 2023Article
- Hangover-Related Internet Searches Before and During the COVID-19 Pandemic in England: Observational Study.JMIR formative research · 2023Article
- Increased Rate of Fracture Injuries Associated With Alternative Modes of Transportation During COVID-19.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2022Article
- COVID-19 hospitalizations forecasts using internet search data.Scientific reports · 2022Article
- Article
- Digital Public Health Surveillance Tools for Alcohol Use and HIV Risk Behaviors.AIDS and behavior · 2021Article
- Technology-Delivered Intervention Strategies to Bolster HIV Testing.Current HIV/AIDS reports · 2021Review
- Power of Big Data in ending HIV.AIDS (London, England) · 2021Article
- Using Search Engine Data to Explore Interest in PrEP and HIV Testing in the United States.AIDS and behavior · 2021Article
- Internet search and medicaid prescription drug data as predictors of opioid emergency department visits.NPJ digital medicine · 2021Article
- Comparing Web-Based Platforms for Promoting HIV Self-Testing and Pre-Exposure Prophylaxis Uptake in High-Risk Men Who Have Sex With Men: Protocol for a Longitudinal Cohort Study.JMIR research protocols · 2020Article
- When Public Health Research Meets Social Media: Knowledge Mapping From 2000 to 2018.Journal of medical Internet research · 2020Review
- Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundA large and growing body of "big data" is generated by internet search engines, such as Google. Because people often search for information about public health and medical issues, researchers may be able to use search engine data to monitor and predict public health problems, such as HIV. We sought to assess the feasibility of using Google search data to analyze and predict new HIV diagnoses cases in the United States. METHODS AND
findingsFrom 2007 to 2014, we collected search volume data on HIV-related Google search keywords across the United States. State-level new HIV diagnoses data were collected from the Centers for Disease Control and Prevention (CDC) and AIDSVu.org. We developed a negative binomial model to predict HIV cases using a subset of significant predictor keywords identified by LASSO. The Google search data were combined with state-level HIV case reports provided by the CDC. We use historical data to train the model and predict new HIV diagnoses from 2011 to 2014, with an average R2 value of 0.99 between predicted versus actual cases, and average root-mean-square error (RMSE) of 108.75.
conclusionsResults indicate that Google Trends is a feasible tool to predict new cases of HIV at the state level. We discuss the implications of integrating visualization maps and tools based on these models into public health and HIV monitoring and surveillance.
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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.