Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks

Scientific Reports

Volumn 7, Issue , 2017, Pages

  Ghosh, Saurav ^a   Chakraborty, Prithwish ^a   Nsoesie, Elaine O ^b,c,d   Cohn, Emily ^b   Mekaru, Sumiko R ^c,e   Brownstein, John S ^b,c,e,f   Ramakrishnan, Naren ^a  

^a VIRGINIA POLYTECHNIC INSTITUTE AND STATE UNIVERSITY   (United States)

^b BOSTON CHILDREN S HOSPITAL   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d UNIVERSITY OF WASHINGTON   (United States)

^e Epidemico Inc   (United States)

^f MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CHINA; COMMUNICABLE DISEASE; DENGUE; EPIDEMIC; HUMAN; INDIA; PATHOLOGY; PERTUSSIS; SEASON; THEORETICAL MODEL; UNITED STATES;

CHINA; COMMUNICABLE DISEASES; DENGUE; DISEASE OUTBREAKS; HUMANS; INDIA; MODELS, THEORETICAL; SEASONS; UNITED STATES; WHOOPING COUGH;

EID: 85009948917     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep40841     Document Type: Article

References (50)

1. Brownstein, J. S., Freifeld, C. C., Reis, B. Y. & Mandl, K. D. Surveillance Sans Frontieres: Internet-based emerging infectious disease intelligence and the Healthmap project. PLoS Medicine 5, e151 (2008).
2. Linge, J. P. et al. Internet surveillance systems for early alerting of health threats. Eurosurveillance 14, 200-201 (2009).
3. Corley, C. D., Cook, D. J., Mikler, A. R. & Singh, K. P. Text and structural data mining of influenza mentions in web and social media. International Journal of Environmental Research and Public Health 7, 596-615 (2010).
4. Yuan, Q. et al. Monitoring influenza epidemics in china with search query from baidu. PLoS One 8, e64323 (2013).
5. Ginsberg, J. et al. Detecting influenza epidemics using search engine query data. Nature 457, 1012-1014 (2009).
6. Santillana, M., Nsoesie, E. O., Mekaru, S. R., Scales, D. & Brownstein, J. S. Using clinicians search query data to monitor influenza epidemics. Clinical Infectious Diseases 59, 1446-1450 (2014).
7. Gu, Y. et al. Early detection of an epidemic erythromelalgia outbreak using baidu search data. Scientific Reports 5 (2015).
8. Denecke, K., Dolog, P. & Smrz, P. Making use of social media data in public health. In Proceedings of the 21st International Conference Companion on World Wide Web, 243-246 (ACM, 2012).
9. Lee, K., Agrawal, A. & Choudhary, A. Real-Time disease surveillance using twitter data: demonstration on flu and cancer. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1474-1477 (ACM, 2013).
10. Sugumaran, R. & Voss, J. Real-Time spatio-Temporal analysis of west nile virus using twitter data. In Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications, 39 (ACM, 2012).
11. Paul, M. J. & Dredze, M. You are what you tweet: Analyzing twitter for public health. In Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, 265-272 (2011).
12. Nsoesie, E. O., Butler, P., Ramakrishnan, N., Mekaru, S. R. & Brownstein, J. S. Monitoring disease trends using hospital traffic data from high resolution satellite imagery: A feasibility study. Scientific Reports 5 (2015).
13. Chakraborty, P. et al. Forecasting a moving target: Ensemble models for ILI case count predictions. In Proceedings of the 2014 SIAM International Conference on Data Mining, 262-270 (SIAM, 2014).
14. Rekatsinas, T. et al. SourceSeer: Forecasting rare disease outbreaks using multiple data sources. In Proceedings of the 2015 SIAM International Conference on Data Mining, 379-387 (SIAM, 2015).
15. Nsoesie, E. O. & Brownstein, J. S. Computational approaches to influenza surveillance: Beyond timeliness. Cell Host & Microbe 17, 275-278 (2015).
16. Salathé, M. et al. Digital epidemiology. PLoS Computational Biology 8, e1002616 (2012).
17. Salathé, M., Freifeld, C. C., Mekaru, S. R., Tomasulo, A. F. & Brownstein, J. S. Influenza A (H7N9) and the importance of digital epidemiology. The New England Journal of Medicine 369, 401-404 (2013).
18. Hartley, D. M. et al. An overview of internet biosurveillance. Clinical Microbiology and Infection 19, 1006-1013 (2013).
19. Freifeld, C. C., Mandl, K. D., Reis, B. Y. & Brownstein, J. S. Healthmap: global infectious disease monitoring through automated classification and visualization of internet media reports. Journal of the American Medical Informatics Association 15, 150-157 (2008).
20. Cherry, J. D. Epidemic pertussis in 2012-The resurgence of a vaccine-preventable disease. The New England Journal of Medicine 367, 785-787 (2012).
21. Yang, S. et al. Avian-origin influenza A (H7N9) infection in influenza A (H7N9)-Affected areas of china: A serological study. Journal of Infectious Diseases 209, 265-269 (2014).
22. Gao, H.-N. et al. Clinical findings in 111 cases of influenza A (H7N9) virus infection. The New England Journal of Medicine 368, 2277-2285 (2013).
23. Shen, S.-Q. et al. Multiple sources of infection and potential endemic characteristics of the large outbreak of dengue in guangdong in 2014. Scientific Reports 5 (2015).
24. Van Panhuis, W. G. et al. Contagious diseases in the united states from 1888 to the present. The New England journal of medicine 369, 2152 (2013).
25. Wan, X. Co-Training for cross-lingual sentiment classification. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1, 235-243 (Association for Computational Linguistics, 2009).
26. Pak, A. & Paroubek, P. Twitter based system: Using twitter for disambiguating sentiment ambiguous adjectives. In Proceedings of the 5th International Workshop on Semantic Evaluation, 436-439 (Association for Computational Linguistics, 2010).
27. Wan, X. Using bilingual knowledge and ensemble techniques for unsupervised chinese sentiment analysis. In Proceedings of the conference on empirical methods in natural language processing, 553-561 (Association for Computational Linguistics, 2008).
28. Webster, J. J. & Kit, C. Tokenization as the initial phase in NLP. In Proceedings of the 14th Conference on Computational Linguistics, vol. 4, 1106-1110 (Association for Computational Linguistics, 1992).
29. Singh, V. & Saini, B. An effective pre-processing algorithm for information retrieval systems. International Journal of Database Management Systems 6, 13 (2014).
30. Kanis, J. & Skorkovská, L. Comparison of different lemmatization approaches through the means of information retrieval performance. In Text, Speech and Dialogue, 93-100 (Springer, 2010).
31. Ramakrishnan, N. et al. Beating the news with EMBERS: Forecasting civil unrest using open source indicators. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1799-1808 (ACM, 2014).
32. Doyle, A. et al. Forecasting significant societal events using the embers streaming predictive analytics system. Big Data 2, 185-195 (2014).
33. Blei, D. M., Ng, A. Y. & Jordan, M. I. Latent dirichlet allocation. The Journal of Machine Learning Research 3, 993-1022 (2003).
34. Mcauliffe, J. D. & Blei, D. M. Supervised topic models. In Advances in Neural Information Processing Systems, 121-128 (2008).
35. Jagarlamudi, J., Daumé III, H. & Udupa, R. Incorporating lexical priors into topic models. In Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics, 204-213 (Association for Computational Linguistics, 2012).
36. Steyvers, M. & Griffiths, T. Probabilistic topic models. Handbook of Latent Semantic Analysis 427, 424-440 (2007).
37. Matsubara, Y., Sakurai, Y., Faloutsos, C., Iwata, T. & Yoshikawa, M. Fast mining and forecasting of complex time-stamped events. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 271-279 (ACM, 2012).
38. Porteous, I. et al. Fast collapsed gibbs sampling for latent dirichlet allocation. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 569-577 (ACM, 2008).
39. Minka, T. & Lafferty, J. Expectation-propagation for the generative aspect model. In Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, 352-359 (Morgan Kaufmann Publishers Inc., 2002).
40. Rosen-Zvi, M., Griffiths, T., Steyvers, M. & Smyth, P. The author-Topic model for authors and documents. In Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, 487-494 (AUAI Press, 2004).
41. Wang, Z. et al. Dynamic poisson autoregression for influenza-like-illness case count prediction. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1285-1294 (ACM, 2015).
42. Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 67, 301-320 (2005).
43. Tibshirani, R. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 267-288 (1996).
44. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning (New York: Springer, 2009).
45. Kerns, G. J. Introduction to probability and statistics using r (Lulu. com, 2010).
46. Box, G. E., Jenkins, G. M. & Reinsel, G. C. Time series analysis: forecasting and control, vol. 734 (John Wiley & Sons, 2011).
47. Lazer, D., Kennedy, R., King, G. & Vespignani, A. The parable of google flu: Traps in big data analysis. Science 343, 1203-1205 (2014).
48. Akil, L., Ahmad, H. A. & Reddy, R. S. Effects of climate change on salmonella infections. Foodborne Pathogens and Disease 11, 974-980 (2014).
49. Curriero, F. C., Patz, J. A., Rose, J. B. & Lele, S. The association between extreme precipitation and waterborne disease outbreaks in the united states, 1948-1994. American Journal of Public Health 91, 1194-1199 (2001).
50. Hales, S., De Wet, N., Maindonald, J. & Woodward, A. Potential effect of population and climate changes on global distribution of dengue fever: An empirical model. The Lancet 360, 830-834 (2002).