ArticleScientific reports2020
Comparing Social media and Google to detect and predict severe epidemics.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Integrating mathematical modelling and artificial intelligence to combat emerging viral syndemics: A systematic review.New microbes and new infections · 2026Review
- Evaluating community resilience through social media during China's first post-COVID-19 reopening: insights from machine learning.Journal of global health · 2025Article
- Early Warning of Infectious Disease Outbreaks Using Social Media and Digital Data: A Scoping Review.International journal of environmental research and public health · 2025Article
- Evolutionary Trend of Dental Health Care Information on Chinese Social Media Platforms During 2018-2022: Retrospective Observational Study.JMIR infodemiology · 2025Observational
- Use of Digital Tools in Arbovirus Surveillance: Scoping Review.Journal of medical Internet research · 2024Article
- Google trends as an early indicator of African swine fever outbreaks in Southeast Asia.Frontiers in veterinary science · 2024Article
- Article
- Using Google Trends to Estimate the Geographic Distribution of Soil-Transmitted Helminthiasis in the United States from 2016 to 2021.Tropical medicine and infectious disease · 2023Article
- Long Short-term Memory-Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation.Journal of medical Internet research · 2023Article
- Can the Content of Social Networks Explain Epidemic Outbreaks?Population research and policy review · 2023Article
- Machine learning based regional epidemic transmission risks precaution in digital society.Scientific reports · 2022Article
- Chimeric forecasting: combining probabilistic predictions from computational models and human judgment.BMC infectious diseases · 2022Article
- Integrating Internet multisource big data to predict the occurrence and development of COVID-19 cryptic transmission.NPJ digital medicine · 2022Article
- Can people hear others' crying?: A computational analysis of help-seeking on Weibo during COVID-19 outbreak in China.Information processing & management · 2022Article
- How Italy Tweeted about COVID-19: Detecting Reactions to the Pandemic from Social Media.International journal of environmental research and public health · 2022Article
- An infodemiological framework for tracking the spread of SARS-CoV-2 using integrated public data.Pattern recognition letters · 2022Article
- Tracking discussions of complementary, alternative, and integrative medicine in the context of the COVID-19 pandemic: a month-by-month sentiment analysis of Twitter data.BMC complementary medicine and therapies · 2022Article
- Evolution of Select Epidemiological Modeling and the Rise of Population Sentiment Analysis: A Literature Review and COVID-19 Sentiment Illustration.International journal of environmental research and public health · 2022Review
- Article
- Perceptions of blame on social media during the coronavirus pandemic.Computers in human behavior · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Internet technologies have demonstrated their value for the early detection and prediction of epidemics. In diverse cases, electronic surveillance systems can be created by obtaining and analyzing on-line data, complementing other existing monitoring resources. This paper reports the feasibility of building such a system with search engine and social network data. Concretely, this study aims at gathering evidence on which kind of data source leads to better results. Data have been acquired from the Internet by means of a system which gathered real-time data for 23 weeks. Data on influenza in Greece have been collected from Google and Twitter and they have been compared to influenza data from the official authority of Europe. The data were analyzed by using two models: the ARIMA model computed estimations based on weekly sums and a customized approximate model which uses daily sums. Results indicate that influenza was successfully monitored during the test period. Google data show a high Pearson correlation and a relatively low Mean Absolute Percentage Error (R = 0.933, MAPE = 21.358). Twitter results are slightly better (R = 0.943, MAPE = 18.742). The alternative model is slightly worse than the ARIMA(X) (R = 0.863, MAPE = 22.614), but with a higher mean deviation (abs. mean dev: 5.99% vs 4.74%).
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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.