Cited by 381Data-Driven Disease SurveillanceInfluenza Virus Research StudiesRespiratory viral infections research

The DOI matches its Crossref record; 9 of the 10 references it cites were found in a registry or an index.

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

Open access

Cook, Samantha, Conrad, Corrie, Fowlkes, Ashley L., Mohebbi, Matthew H.·2011·PLoS ONE

DOI 10.1371/journal.pone.0023610PMC PMC3158788

Open this paper in CoChat — free account Open PDF

Abstract

BACKGROUND: Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1. METHODOLOGY/PRINCIPAL FINDINGS: We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009-Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications. CONCLUSIONS: Internet search behavior changed during pH1N1, particularly in the categories "influenza complications" and "term for influenza." The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1371/journal.pone.0023610

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

MapReduce: simplified data processing on large clusters
Dean, J., & Ghemawat, S. (2008) · Communications of the ACM · doi:10.1145/1327452.1327492 · Cited by 18,476
Detecting influenza epidemics using search engine query data
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009) · Nature · doi:10.1038/nature07634 · Cited by 4,295
Self-citation
Emergence of a Novel Swine-Origin Influenza A (H1N1) Virus in Humans
Novel Swine-Origin Influenza A (H1N1) Virus Investigation Team (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0903810 · Cited by 3,063
Infodemiology: tracking flu-related searches on the web for syndromic surveillance.
Eysenbach, G. (2006) · PubMed · Cited by 414
Interim analysis of pandemic influenza (H1N1) 2009 in Australia: surveillance trends, age of infection and effectiveness of seasonal vaccination
Kelly, H., & Grant, K. A. (2009) · Eurosurveillance · doi:10.2807/ese.14.31.19288-en · Cited by 193
Interpreting “Google Flu Trends” data for pandemic H1N1 influenza: The New Zealand experience
Wilson, N., Mason, K., Tobias, M., Peacey, M., Huang, Q. S., & Baker, M. G. (2009) · Eurosurveillance · doi:10.2807/ese.14.44.19386-en · Cited by 78
Surveillance for Influenza during the 2009 Influenza A (H1N1) Pandemic-United States, April 2009-March 2010
Brammer, L., Blanton, L., Epperson, S., Mustaquim, D., Bishop, A., Kniss, K. L., Dhara, R., Nowell, M., Kamimoto, L., & Finelli, L. (2010) · Clinical Infectious Diseases · doi:10.1093/cid/ciq009 · Cited by 67
Influenza activity--United States and worldwide, 2007-08 season.
(2008) · PubMed · Cited by 63
Influenza-Like Illness in the Community during the Emergence of 2009 Pandemic Influenza A(H1N1) – Survey of 10 States, April 2009
Reed, C., Angulo, F. J., Biggerstaff, M., Swerdlow, D. L., & Finelli, L. (2010) · Clinical Infectious Diseases · doi:10.1093/cid/ciq024 · Cited by 16

Flagged first, then by citations

Show all 10 references

1 of the 10 references couldn’t be matched to a known paper.

Sorted by citations

Items 201–300 of 379

Modernizing the Methods and Analytics Curricula for Health Science Doctoral Programs
Dinov, I. D. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00022 · Cited by 12
Reporting and Transparency in Big Data: The Nexus of Ethics and Methodology
Nicholls, S. G., Langan, S., & Benchimol, E. I. (2016) · Law, governance and technology series · doi:10.1007/978-3-319-33525-4_15 · Cited by 12
Online interest regarding violent attacks, gun control, and gun purchase: A causal analysis
Gunn, L. H., ter Horst, E., Markossian, T. W., & Molina, G. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207924 · Cited by 11
Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study
Jang, B., Kim, I., & Kim, J. W. (2021) · JMIR Medical Informatics · doi:10.2196/23305 · Cited by 11
Role of Online Data from Search Engine and Social Media in Healthcare Informatics
Nawaz, M. S., Mustafa, R. U., & Lali, M. I. U. (2017) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-5225-2607-0.ch011 · Cited by 10
PEACOCK: A Map-Based Multitype Infectious Disease Outbreak Information System
Jang, B., Lee, M., & Kim, J. W. (2019) · IEEE Access · doi:10.1109/access.2019.2924189 · Cited by 10
Detecting influenza states based on hybrid model with personal emotional factors from social networks
Sun, X. N., Ye, J., & Ren, F. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.01.107 · Cited by 10
Tracking U.S. Pertussis Incidence: Correlation of Public Health Surveillance and Google Search Data Varies by State
Arehart, C. H., David, M. Z., & Dukić, V. (2019) · Scientific Reports · doi:10.1038/s41598-019-56385-z · Cited by 10
Measuring the Strength of Attitudes in Social Media Data
Amaya, A., Bach, R. L., Kreuter, F., & Keusch, F. (2020) · doi:10.1002/9781118976357.ch5 · Cited by 10
Temporal Trends and Interest in Coronary Artery Calcium Scoring Over Time: An Infodemiology Study
Dzaye, O. D. A., Berning, P., Adelhoefer, S. J., Duebgen, M., Blankstein, R., Mahesh, M., Nasir, K., Blumenthal, R. S., Mortensen, M. B., & Blaha, M. J. (2021) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2021.02.010 · Cited by 9
Predicting influenza-like illness-related emergency department visits by modelling spatio-temporal syndromic surveillance data
Martin, L. J., Dong, H., Liu, Q., Talbot, J., Qiu, W., & Yasui, Y. (2019) · Epidemiology and Infection · doi:10.1017/s0950268819001948 · Cited by 9
Use and interest of electronic nicotine delivery systems (ENDS): Assessing the validity of Google Trends
Ghosh, A., Kaur, S., & Roub, F. (2021) · The American Journal of Drug and Alcohol Abuse · doi:10.1080/00952990.2021.1944171 · Cited by 8
Analyzing Cyberchondriac Google Trends Data to Forecast Waves and Avoid Friction: Lessons From COVID-19 in India
Rao, A., Sharma, G. D., Pereira, V. E., Shahzad, U., & Jabeen, F. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3147375 · Cited by 9
Computational challenges for sentiment analysis in life sciences
Ciullo, F., Zucco, C., Calabrese, B., Agapito, G., Guzzi, P. H., & Cannataro, M. (2016) · International Conference on High Performance Computing & Simulation (HPCS) · doi:10.1109/hpcsim.2016.7568365 · Cited by 9
Using Sequence Mining to Predict Complex Systems: A Case Study in Influenza Epidemics
Aldhyani, T. H. H., Joshi, M., Maaytah, S. A. A., Alqarni, A. A., & Alsharif, N. H. (2021) · Complexity · doi:10.1155/2021/9929013 · Cited by 9
Data, tools and resources for mining social media drug chatter
Sarker, A., & Gonzalez‐Hernandez, G. (2016) · International Conference on Computational Linguistics · Cited by 9
Online Information Search During COVID-19Preprint
Lucas, B., Elliot, B., & Landman, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.07183 · Cited by 13
Processing unstructured documents and social media using Big Data techniques
Dıaconıța, V. (2015) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2015.1095110 · Cited by 8
Predicting COVID-19 new cases in California with Google Trends data and a machine learning approach
Habibdoust, A., Seifaddini, M., Tatar, M., Araz, Ö. M., & Wilson, F. A. (2024) · Informatics for Health and Social Care · doi:10.1080/17538157.2024.2315246 · Cited by 8
An infodemiology study on breast cancer in Iran
SeyyedHosseini, S., Asemi, A., Shabani, A., & CheshmehSohrabi, M. (2018) · The Electronic Library · doi:10.1108/el-03-2017-0062 · Cited by 8
Beyond Artificial Reality
Pu, C., Suprem, A., Lima, R. A., Musaev, A., Wang, D., Irani, D., Webb, S., & Ferreira, J. E. (2020) · ACM Transactions on Internet Technology · doi:10.1145/3374214 · Cited by 11
Can Users Search Trends Predict People Scares or Disease Breakout? An Examination of Infectious Skin Diseases in the United States
Obeidat, R., Alsmadi, I. M., Baker, Q. B., & Obeidat, L. M. (2020) · Infectious Diseases Research and Treatment · doi:10.1177/1178633720928356 · Cited by 7
Hybrid Model Based Influenza Detection with Sentiment Analysis from Social Networks
Sun, X. N., Ye, J., & Ren, F. (2015) · Communications in computer and information science · doi:10.1007/978-981-10-0080-5_5 · Cited by 8
Using Secondary Data to Tell a New Story: A Cautionary Tale in Health Information Technology Research
Sarkar, S., Ghosh, K., & Petter, S. (2020) · Communications of the Association for Information Systems · doi:10.17705/1cais.04705 · Cited by 8
Supplementary Materials for The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D. M. J., Kennedy, R., King, G., & Vespignani, A. (2014) · Cited by 8
Searching for the Peak Google Trends and the COVID-19 Outbreak in ItalyPreprint
Brunori, P., Resce, G., & Serlenga, L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3569909 · Cited by 8
Service Ecosystem: A Lens of Smart Digital Society
Xue, X., Feng, Z., Chen, S., Zhou, Z., Qin, C., Li, B., Wang, Z., Wang, S., Wu, H., Zhang, L., et al. (2021) · IEEE International Conference on Services Computing · doi:10.1109/scc53864.2021.00039 · Cited by 7
The Production of Volunteered Geographic Information: A Study of OpenStreetMap in the United States
Parr, D. A. (2015) · Cited by 7
Decoding the dynamics of knowledge pricing: An empirical investigation of MOOC platform edX's pricing structure and signaling effects
Shi, Y., Armin Haller, A., Reeson, A. F., & Li, X. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100220 · Cited by 7
Nowcasting Credit Demand in Turkey with Google Trends Data
Zeybek, Ö., & Uğurlu, E. (2015) · RePEc: Research Papers in Economics · Cited by 7
Big data and the historical sciences: A critique
Ebach, M. C., Michael, M. S., Shaw, W. S., GOFF, J. R., Murphy, D. J., & Matthews, S. T. (2016) · Geoforum · doi:10.1016/j.geoforum.2016.02.020 · Cited by 7
Using Transactional Big Data for Epidemiological Surveillance: Google Flu Trends and Ethical Implications of ‘Infodemiology’
Richterich, A. (2016) · Law, governance and technology series · doi:10.1007/978-3-319-33525-4_3 · Cited by 7
A Real Time Epidemic Alert Generation System for Rural Areas Using WBANs and Kiosks
Giri, S., Dutta, S. R., Datta, S., & Roy, M. (2017) · doi:10.1109/icit.2017.19 · Cited by 7
Algorithmic futures
Duclos, V. (2019) · Medicine Anthropology Theory · doi:10.17157/mat.6.3.660 · Cited by 10
Examining the Correlation of Google Influenza Trend with Hospital Data: Retrospective Study
Jabour, A. M., Varghese, J., Damad, A. H., Ghailan, K. Y., & Mehmood, A. (2021) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s322185 · Cited by 7
Big data and psychology in China
Yu, F., Peng, K., & Zheng, X. S. (2015) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/n972014-00977 · Cited by 7
DataSnap: Enabling Domain Experts and Introductory Programmers to Process Big Data in a Block-Based Programming Language
Hellmann, J. D. (2015) · VTechWorks (Virginia Tech) · Cited by 7
Whiplash Syndrome Reloaded: Digital Echoes of Whiplash Syndrome in the European Internet Search Engine Context
Noll‐Hussong, M. (2017) · JMIR Public Health and Surveillance · doi:10.2196/publichealth.7054 · Cited by 6
Predicting US state teenage birth rates using search engine query data on pregnancy termination and prevention
Reidpath, D. D., & Allotey, P. A. (2018) · Journal of Global Health Reports · doi:10.29392//001c.11938 · Cited by 6
Big Data and Biodefense: Prospects and Pitfalls
Vogel, K. M. (2019) · Springer eBooks · doi:10.1007/978-3-030-03053-7_15 · Cited by 6
Using agricultural metadata: a novel investigation of trends in sowing date in on-farm research trials using the Online Farm Trials database
Walters, J. R., Light, K., & Robinson, N. J. (2020) · F1000Research · doi:10.12688/f1000research.26903.2 · Cited by 6
Exploration of Content and Reach of Physical Therapy-Related Discussion on Twitter
Sabus, C. H., Johns, B., Schultz, N., & Gagnon, K. (2019) · Physical Therapy · doi:10.1093/ptj/pzz063 · Cited by 5
Google trends as an early indicator of African swine fever outbreaks in Southeast Asia
Hsu, C.-H., Yang, C.-H., & Perez, A. M. (2024) · Frontiers in Veterinary Science · doi:10.3389/fvets.2024.1425394 · Cited by 5
Public Mood and Consumption Choices: Evidence from Sales of Sony Cameras on Taobao
Ma, Q., & Zhang, W. (2015) · PLoS ONE · doi:10.1371/journal.pone.0123129 · Cited by 5
Privacy Is Healthy
Caine, K. E. (2016) · IEEE Pervasive Computing · doi:10.1109/mprv.2016.61 · Cited by 5
Trends detection of flu based on ensemble models with emotional factors from social networks
Sun, X. N., Ren, F., & Ye, J. (2016) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.22389 · Cited by 5
Predicting Antimicrobial Drug Consumption using Web Search Data
Hansen, N. D., Mølbak, K., Cox, I. J., & Lioma, C. (2018) · International Conference on Digital Health · doi:10.1145/3194658.3194667 · Cited by 5
Google arama hacmi verileri ile Türkiye’de hastalık farkındalık günlerinin etkinliğinin değerlendirilmesi
Donar, G. B. (2020) · Mersin üniversitesi sağlık bilimleri dergisi/Mersin üniversitesi sağlık bilimleri dergisi · doi:10.26559/mersinsbd.691939 · Cited by 4
Internet search data with spatiotemporal analysis in infectious disease surveillance: Challenges and perspectives
Sun, H., Zhang, Y., Gao, G., & Wu, D. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.958835 · Cited by 5
Social media applications in biomedical research
Hussain, M. S., & Tewari, D. (2024) · Exploration of Digital Health Technologies · doi:10.37349/edht.2024.00019 · Cited by 5
Presidential Address: Borne of a Renaissance--A Metamorphosis for Our Future
Santos, R. L. (2014) · Public Opinion Quarterly · doi:10.1093/poq/nfu034 · Cited by 5
Big Data and Biomedicine
Levin, N. (2017) · Palgrave Macmillan UK eBooks · doi:10.1057/978-1-137-52879-7_28 · Cited by 5
Data Analytics For Forecasting Arrival of Tourism Visit in Indonesia
Haqiq, A., & Pharmasetiawan, B. (2019) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss48059.2019.8969795 · Cited by 5
Fever and mobility data indicate social distancing has reduced incidence of communicable disease in the United StatesPreprint
Liautaud, P. R., Huybers, P., & Santillana, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.09911 · Cited by 9
Google Trends Analysis of COVID-19Preprint
Nguyen, H. L., Pan, Z., Abu-gellban, H., Jin, F., & Zhang, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.03847 · Cited by 5
Attempts at Enhancing eVision’s Influenza Forecasting Using Social Media
Shaghaghi, N., Kamdar, Y., Huang, R., Calle, A., Mirchandani, J., & Castillo, M. (2022) · Biomedical Engineering International Conference · doi:10.1109/bmeicon56653.2022.10012095 · Cited by 4
Public interest in aquatic foods in the United States: A study of internet search activity
Garlock, T., LaFontaine, Q., & Asche, F. (2025) · Aquaculture · doi:10.1016/j.aquaculture.2025.742862 · Cited by 4
Big Data in Academic Research: Challenges, Pitfalls, and Opportunities
Raubenheimer, J. (2021) · Policy implications of research in education · doi:10.1007/978-3-030-76841-6_1 · Cited by 5
Media Reports as a Source for Monitoring Impact of Influenza on Hospital Care
Reukers, D. F. M., Marbus, S. D., Smit, H., Schneeberger, P. M., Donker, G., van der Hoek, W., & van Gageldonk‐Lafeber, A. B. (2019) · JMIR Public Health and Surveillance · doi:10.2196/14627 · Cited by 4
Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic
Morsi, S. A., & Alzahrani, M. E. (2022) · Applied Bionics and Biomechanics · doi:10.1155/2022/6056574 · Cited by 4
Retracted
Nowcasting Credit Demand in Turkey with Google Trends DataPreprint
Uğurlu, E. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3476406 · Cited by 4
Electronic Surveillance
Moorlock, G. (2015) · Springer eBooks · doi:10.1007/978-3-319-05544-2_167-1 · Cited by 4
Incorporating connectivity among Internet search data for enhanced influenza-like illness tracking
Ning, S., Hussain, A., & Wang, Q. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305579 · Cited by 3
Metrics and Evaluation Tools for Communicable and Non-communicable Diseases
Lu, L. (2020) · Springer eBooks · doi:10.1007/978-3-030-59687-3_12 · Cited by 3
Determinants of Public Interest in Emerging and Re-Emerging Arboviral Diseases in Europe
Allgoewer, K. (2022) · Journal of preventive medicine and hygiene · doi:10.15167/2421-4248/jpmh2022.63.4.2736 · Cited by 3
Tweeting during the Pandemic in New York City: Unveiling the Evolving Sentiment Landscape of NYC through a Spatiotemporal Analysis of Geolocated Tweets
Ignaccolo, C., Wibisono, K. C., Sutto, M. P., & Plunz, R. A. (2024) · Journal of Urban Technology · doi:10.1080/10630732.2024.2364569 · Cited by 3
Influenza outbreaks in Singapore: epidemiology, diagnosis, treatment and prevention
Tang, J. W.-T., Loh, T. P., Tambyah, P. A., & Koay, E. S.-C. (2012) · Expert Review of Anti-infective Therapy · doi:10.1586/eri.12.63 · Cited by 3
Introducing Big Data
Lake, P., & Drake, R. G. (2014) · Advanced information and knowledge processing · doi:10.1007/978-3-319-13503-8_1 · Cited by 3
Geospatial Applications in Disease Surveillance
Blatt, A. J. (2013) · International Journal of Applied Geospatial Research · doi:10.4018/jagr.2013040101 · Cited by 3
Periodic Trends in Internet Searches for Ocular Symptoms in the US
Oke, I., Gaier, E. D., Mantagos, I. S., & Shah, A. S. (2022) · Ophthalmic Epidemiology · doi:10.1080/09286586.2022.2119260 · Cited by 3
A Survey on Digital Epidemiology: Networks and Models
Pastor-Escuredo, D., & Olmos, L. E. (2022) · Medical Research Archives · doi:10.18103/mra.v10i10.3175 · Cited by 5
Rethinking the role of uncertainty and risk in Marketing
Grimaldi, D., Sallan, J. M., Arboleda, H., & Sehgal, S. (2023) · Journal of Decision System · doi:10.1080/12460125.2023.2232570 · Cited by 8
Marburg Virus Outbreak and a New Conspiracy Theory: Findings from a Comprehensive Analysis and Forecasting of Web Behavior
Thakur, N., Cui, S., Patel, K. A., Azizi, N., Knieling, V., Han, C., Poon, A., & Shah, R. (2023) · Computation · doi:10.3390/computation11110234 · Cited by 3
Discovering Multi-Scale Co-Occurrence Patterns of Asthma and Influenza with Oak Ridge Bio-Surveillance Toolkit
Ramanathan, A., Pullum, L., Hobson, T. C., Stahl, C. G., Steed, C. A., Quinn, S. P., Chennubhotla, C. S., & Valkova, S. (2015) · Frontiers in Public Health · doi:10.3389/fpubh.2015.00182 · Cited by 3
Using open data and Google search data for competitive intelligence analysis
Černýa, J., Potančoka, M., & Molnára, Z. (2019) · Journal of Intelligence Studies in Business · doi:10.37380/jisib.v9i2.470 · Cited by 3
Google Trends (GT) related to influenza
Wiwanitkit, V. (2015) · Cadernos de Saúde Pública · doi:10.1590/0102-311xca020615 · Cited by 3
ARGO: a model for accurate estimation of influenza epidemics using Google search dataPreprint
Yang, S., Santillana, M., & Kou, S. C. (2015) · arXiv (Cornell University) · Cited by 3
A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones
Susuta, R., Yamada, K., Hideki Takayasu, C., & Takayasu, M. (2025) · Entropy · doi:10.3390/e27070686 · Cited by 2
Optimizing neural networks to predict the impact of demographic heterogeneity on nonlinear fractional H1N1 transmission involving climate modeling
Rasheed, R., Ali, I., Khalid, A., Rashid, S., & Hassaballa, A. A. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117060 · Cited by 2
Intrest in influenza and influenza like illnesses in Poland 2016-2021 based on Google Trend’s data analysis
Sycińska-Dziarnowska, M., Woźniak, K., & Paradowska-Stankiewicz, I. A. (2022) · Przeglad Epidemiologiczny · doi:10.32394/pe.76.02 · Cited by 2
A look back: investigating Google Flu Trends during the influenza A(H1N1)pdm09 pandemic in Canada, 2009–2010
Martin, L. J. (2016) · Epidemiology and Infection · doi:10.1017/s0950268816002636 · Cited by 2
Innovación, big data y epidemiología
Casacuberta, D. (2023) · Revista Iberoamericana de Argumentación · doi:10.15366/ria2013.7.004 · Cited by 2
En salud pública, una persona famosa puede influir tanto como una campaña internacional
Basagoiti, I., Fernández-Luque, L., Ruiz-Baqués, A., & Ayala, A. (2013) · Gaceta Sanitaria · doi:10.1016/j.gaceta.2013.01.009 · Cited by 2
A framework for comparing early warning systems across domains: A step toward a data-integrated public health EWS
Kim, H., Laskowski, M., Moghadas, S. M., Sajad, A., & Asif, M. (2014) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2014.7051875 · Cited by 2
Assessing E-Government Success Strategies using Internet Search Data
Boland, K. M., & McNutt, J. G. (2013) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/978-1-4666-4058-0.ch016 · Cited by 2
Research on College Students' Psychological Crisis Intervention in the Context of Big Data
Jiang, P. (2018) · International Conference on Big Data and Education (ICBDE) · doi:10.1145/3206157.3206173 · Cited by 2
The Historical Background for Mandatory Reporting Laws in Public Health
Richards, E. P. (2015) · Child maltreatment · doi:10.1007/978-94-017-9685-9_6 · Cited by 2
Assessing E-Government Success Strategies using Internet Search Data
Boland, K. M., & McNutt, J. G. (2015) · IGI Global eBooks · doi:10.4018/978-1-4666-8358-7.ch056 · Cited by 2
A LASSO-Based Prediction Model for Child Influenza Epidemics: A Case Study of Shanghai, China
Zhu, J. C., Xu, Y. S., Yu, G., Gao, J., Liu, Y., Cheng, D., Song, C., Chen, J., & Pei, T. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/1775630 · Cited by 2
Policies and incentives for adoption: toward broader use
Rahurkar, S., Jonnalagadda, P., Tutt, J. K., Dixon, B. E., & Menachemi, N. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90802-3.00010-1 · Cited by 2
A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions
Thakur, N., Patel, K. A., Hall, I., Duggal, Y. N., & Cui, S. (2023) · Data · doi:10.3390/data8110163 · Cited by 2
Search engines, news wires and digital epidemiology: Presumptions and facts
Kaveh-Yazdy, F., & Zareh-Bidoki, A.-M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.03.017 · Cited by 2
Development of a Real-Time Estimate of Flu Activity in the United States Using Dynamically Updated Lasso Regressions and Google Search Queries
Zhang, W. (2013) · Cited by 2
Research of the Correlation between the H1N1 Morbidity Data and Google Trends in EgyptPreprint
Li, S., & Zhou, X. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.05300 · Cited by 2
Spatial Panel VAR and Application to Forecast Influenza Incidence Rates of US StatesPreprint
Xie, W. (2015) · SSRN Electronic Journal · doi:10.2139/ssrn.2646870 · Cited by 2
Google Flu
Lacey, K. (2017) · Springer eBooks · doi:10.1007/978-3-319-32001-4_106-1 · Cited by 2
Big Data and Doctoral Research
Berry, R. C., & Johnston, L. (2018) · Advances in library and information science (ALIS) book series · doi:10.4018/978-1-5225-7065-3.ch004 · Cited by 1
Dijital epidemiyoloji
Donar, G. B. (2020) · Türkiye Halk Sağlığı Dergisi · doi:10.20518/tjph.656035 · Cited by 1
Agentenspiele
Vehlken, S., & Pias, C. (2014) · Springer eBooks · doi:10.1007/978-3-658-02523-6_10 · Cited by 1
A Planetary Defense Gateway for Smart Discovery of relevant Information for Decision Support
Bambacus, M., Yang, C. P., Leung, R. Y., Barbee, B. W., Nuth, J. A., Seery, B. D., Jiang, Y., Qin, H., Li, Y., Yu, M., et al. (2017) · NASA Technical Reports Server (NASA) · Cited by 1

Authors: Cook, S., Conrad, C., Fowlkes, A. L., & Mohebbi, M. H.
Affiliations: Google (United States) (US)
Google (United Kingdom) (GB)
Centers for Disease Control and Prevention (US)
Corresponding author: Conrad, Corrie
ORCID: Fowlkes, Ashley L.
Venue: PLoS ONE
Publisher: Public Library of Science (PLoS)
Published: 2011-08-19
Volume: 6
Issue: 8
Pages: e23610
ISSN: 1932-6203 (ISSN-L)
DOI: 10.1371/journal.pone.0023610
PMC: PMC3158788
OpenAlex: W1998956117
Cited by: 381
References: 10
Citations by year: 2026: 5 · 2025: 4 · 2024: 7 · 2023: 8 · 2022: 25 · 2021: 33 · 2020: 50 · 2019: 25 · 2018: 26 · 2017: 34 · 2016: 41 · 2015: 48 · 2014: 38 · 2013: 27 · 2012: 9
FWCI: 18.08
Type: journal article
Language: en
Open access: gold
doi.org
Licence: public-domain
creativecommons.org/publicdomain/zero/1.0/ (unspecified)
Locations: PLoS ONE (publishedVersion, public-domain)
Europe PMC (PubMed Central) (publishedVersion, public-domain)
PubMed Central (publishedVersion, public-domain)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Google
Topics: Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
Health Sciences › Medicine › Epidemiology › Influenza Virus Research Studies
Health Sciences › Medicine › Epidemiology › Respiratory viral infections research
Keywords: Google Flu Trends, influenza surveillance, 2009 H1N1 pandemic, influenza-like illness, online search behavior, health-seeking behavior, model accuracy assessment
MeSH terms: Humans, Influenza, Human, Models, Biological, Population Surveillance, Time Factors, United States, Influenza A Virus, H1N1 Subtype, Search Engine, Pandemics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Assessing Google Flu Trends Performance in the United States during the 2009 Influenza Virus A (H1N1) Pandemic

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks