ArticleJournal of multidisciplinary healthcare2021
Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study.
Article in Journal of multidisciplinary healthcare, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- MRI Waiting Time Disparities in Saudi Public Hospitals: A Six-Year Retrospective Study.Cureus · 2026Article
- LSTCNet: timely prediction of influenza in China using Baidu index and LSTM-TCN hybrid network.BMC public health · 2026Article
- Impact of Demographic and Clinical Subgroups in Google Trends Data: Infodemiology Case Study on Asthma Hospitalizations.Journal of medical Internet research · 2025Article
- Measles and Pertussis outbreaks in England and Wales: a time-series analysis.NIHR open research · 2024Article
- Predicting incidence of hepatitis E for thirteen cities in Jiangsu Province, China.Frontiers in public health · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionMany studies have explored social media and users search activities such as Google Trends to predict and detect influenza activities. Studies that examined Google Trends correlation with the actual hospital influenza cases were conducted in non-tropical regions that have clearly defined seasons. Tropical areas are known for having less-defined seasonality and the extent of Google Trends concordance with actual influenza cases is unknown for these areas. The goal of this study is to compare Google Trends with hospital cases in tropical regions.
methodsWe analyzed 48,263 influenza cases in the time period of 2010 to 2019. The cases were retrieved from central hospital medical records in tropical regions using the corresponding codes for influenza ICD-10 AM. Cases from the medical records were compared with Google Trends to determine trends, seasonality, and correlation.
resultsGraphically, there were some similar areas of the trend, but cross-correlation analysis did not show any significant correlation between hospital and Google Trends with a maximum correlation rate of 0.300. Seasonality analysis showed a clear pattern that peaked around November in Google Trends while hospital data showed less defined seasonality with a smaller peak occurring at the end of December and beginning of January.
conclusionBased on the results, there is a weak correlation between Google Trends and hospital data. More innovative methods are emerging to predict influenza activity using social media and user search data and further study is needed to examine the concurrent trends derived using these methods across regions that have different humidity levels and temperatures.
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