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Machines and Society

A growing guide on the latest in data-driven research and emerging technologies at the intersection of society, information and technology.


In this section, we begin with the seminal case of Google Flu Trends. It was a surveillance tool that Google launched in 2008 to estimate influenza activity in near-real time. GFT models raised hope of faster and easier estimates than "old-school" methods of data collection and statistical analysis, claiming the ascendance of data-driven approaches. In 2015, however, after several major stumbles in subsequent influenza seasons, GFT stopped publishing estimates.

But scientific efforts on harnessing search queries to nowcast or forecast epidemics have continued to the present. We review publications along these lines of research. We focus on one issue, the "blue team dynamics". This describes a process where the algorithm producing the data has been modified by the service provider in accordance with their business model, inducing specific user behaviors and introducing patterns into data.

More generally, beyond the "blue team dynamics", we also discuss benefits and biases, and promise and potential perils of social research using big data for disease nowcasting and forecasting.

Digital Disease Surveillance


Aiello, A. E., Renson, A., & Zivich, P. (2020). Social Media- and Internet-based Disease Surveillance for Public Health. Annual Review of Public Health, 41, 101.


Budd, J., Miller, B. S., Manning, E. M., Lampos, V., Zhuang, M., Edelstein, M., ... & McKendry, R. A. (2020). Digital Technologies in the Public-health Response to COVID-19. Nature Medicine, 26(8), 1183-1192.


Simonsen, L., Gog, J. R., Olson, D., & Viboud, C. (2016). Infectious Disease Surveillance in the Big Data Era: Towards Faster and Locally Relevant Systems. The Journal of Infectious Diseases, 214(suppl_4), S380-S385.


Althouse, B. M., Scarpino, S. V., Meyers, L. A., Ayers, J. W., Bargsten, M., Baumbach, J., ... & Wesolowski, A. (2015). Enhancing Disease Surveillance with Novel Data Streams: Challenges and Opportunities. EPJ Data Science, 4(1), 1-8.


Gasser, U., Ienca, M., Scheibner, J., Sleigh, J., & Vayena, E. (2020). Digital Tools against COVID-19: Taxonomy, Ethical Challenges, and Navigation Aid. The Lancet Digital Health, 2(8), e425-e434.


Groseclose, S. L., & Buckeridge, D. L. (2017). Public Health Surveillance Systems: Recent Advances in Their Use and Evaluation. Annual Review of Public Health, 38, 57-79.

Google Flu Trends: Models


Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting Influenza Epidemics Using Search Engine Query Data. Nature, 457(7232), 1012-1014.


Cook, S., Conrad, C., Fowlkes, A. L., & Mohebbi, M. H. (2011). Assessing Google Flu Trends Performance in the United States During the 2009 Influenza Virus A (H1N1) Pandemic. PloS One, 6(8), e23610.

Google Flu Trends: Critiques


Butler, D. (2013). When Google Got Flu Wrong: US Outbreak Foxes a Leading Web-based Method for Tracking Seasonal Flu. Nature, 494(7436), 155-157.


Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The Parable of Google Flu: Traps in Big Data Analysis. Science, 343(6176), 1203-1205.


Ortiz, J. R., Zhou, H., Shay, D. K., Neuzil, K. M., Fowlkes, A. L., & Goss, C. H. (2011). Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends. PloS One, 6(4), e18687.

Epidemic Forecasts Using Internet Searches


Santillana, M., Zhang, D. W., Althouse, B. M., & Ayers, J. W. (2014). What Can Digital Disease Detection Learn from (An External Revision to) Google Flu Trends?. American Journal of Preventive Medicine, 47(3), 341-347.


Yang, S., Santillana, M., & Kou, S. C. (2015). Accurate Estimation of Influenza Epidemics Using Google Search Data via ARGO. Proceedings of the National Academy of Sciences, 112(47), 14473-14478.


Ma, S., & Yang, S. (2022). Covid-19 Forecasts Using Internet Search Information in the United States. Scientific Reports, 12(1), 1-16.