Towards understanding global spread of disease from everyday interpersonal interactions

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 2783-2789

  Brennan, Sean ^a   Sadilek, Adam ^a   Kautz, Henry ^a  

^a University of Rochester   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL EPIDEMIOLOGIES; DISEASE SPREAD; INFECTIOUS DISEASE; LATENT VARIABLE; MONITORING AND FORECAST; SPREAD OF DISEASE; STATUS UPDATES; USER PARTICIPATION;

ARTIFICIAL INTELLIGENCE;

EPIDEMIOLOGY;

EID: 84896063387     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (27)

1. L. Bettencourt and G.West. A unified theory of urban living. Nature, 467(7318):912-913, 2010.
2. J.S. Brownstein, C.J. Wolfe, and K.D. Mandl. Empirical evidence for the effect of airline travel on inter-regional influenza spread in the united states. PLoS medicine, 3(10):e401, 2006.
3. R. Chunara, J.R. Andrews, and J.S. Brownstein. Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. The American Journal of Tropical Medicine and Hygiene, 86(1):39-45, 2012.
4. V. Colizza, A. Barrat, M. Barthélemy, and A. Vespignani. The role of the airline transportation network in the prediction and predictability of global epidemics. Proceedings of the National Academy of Sciences of the United States of America, 103(7):2015, 2006.
5. N. Collier, N.T. Son, and N.M. Nguyen. OMG U got flu? Analysis of shared health messages for bio-surveillance. Journal of Biomedical Semantics, 2011.
6. C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273-297, 1995.
7. A. Culotta. Towards detecting influenza epidemics by analyzing Twitter messages. In Proceedings of the First Workshop on Social Media Analytics, pages 115- 122. ACM, 2010.
8. J.M. Epstein, D.M. Goedecke, F. Yu, R.J. Morris, D.K. Wagener, and G.V. Bobashev. Controlling pandemic flu: the value of international air travel restrictions. PLoS One, 2(5):e401, 2007.
9. S. Eubank, H. Guclu, VS Anil Kumar, M.V. Marathe, A. Srinivasan, Z. Toroczkai, and N. Wang. Modelling disease outbreaks in realistic urban social networks. Nature, 429(6988):180-184, 2004.
10. C.C. Freifeld, R. Chunara, S.R. Mekaru, E.H. Chan, T. Kass-Hout, A.A. Iacucci, and J.S. Brownstein. Participatory epidemiology: use of mobile phones for community-based health reporting. PLoS medicine, 7(12):e1000376, 2010.
11. J. Ginsberg, M.H. Mohebbi, R.S. Patel, L. Brammer, M.S. Smolinski, and L. Brilliant. Detecting influenza epidemics using search engine query data. Nature, 457(7232):1012-1014, 2008.
12. R.F. Grais, J. Hugh Ellis, and G.E. Glass. Assessing the impact of airline travel on the geographic spread of pandemic influenza. European journal of epidemiology, 18(11):1065-1072, 2003.
13. T.D. Hollingsworth, N.M. Ferguson, and R.M. Anderson. Frequent travelers and rate of spread of epidemics. Emerging infectious diseases, 13(9):1288, 2007.
14. T. Joachims. A support vector method for multivariate performance measures. In ICML 2005, pages 377-384. ACM, 2005.
15. V. Lampos, T. De Bie, and N. Cristianini. Flu detector-tracking epidemics on Twitter. Machine Learning and Knowledge Discovery in Databases, pages 599-602, 2010.
16. M.R. Moser, T.R. Bender, H.S. Margolis, G.R. Noble, A.P. Kendal, and D.G. Ritter. An outbreak of influenza aboard a commercial airliner. American Journal of Epidemiology, 110(1):1-6, 1979.
17. M.E.J. Newman. Spread of epidemic disease on networks. Physical Review E, 66(1):016128, 2002.
18. C. Nicolaides, L. Cueto-Felgueroso, M.C. González, and R. Juanes. A metric of influential spreading during contagion dynamics through the air transportation network. PloS one, 7(7):e40961, 2012.
19. M.J. Paul and M. Dredze. A model for mining public health topics from Twitter. Technical Report. Johns Hopkins University. 2011., 2011.
20. J. Platt et al. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61-74, 1999.
21. POST. Statistical information on air passenger numbers and characteristics http://www.parliament.uk/documents/post/e3.pdf. 2000.
22. S. Ruan, W. Wang, S.A. Levin, et al. The effect of global travel on the spread of sars. Mathematical Biosciences and Engineering, 3(1):205, 2006.
23. Adam Sadilek, Henry Kautz, and Vincent Silenzio. Modeling spread of disease from social interactions. In Sixth AAAI International Conference on Weblogs and Social Media (ICWSM), 2012.
24. Adam Sadilek, Henry Kautz, and Vincent Silenzio. Predicting disease transmission from geo-tagged micro-blog data. In Twenty-Sixth AAAI Conference on Artificial Intelligence, 2012.
25. S. Shalev-Shwartz, Y. Singer, and N. Srebro. Pegasos: Primal estimated sub-gradient solver for svm. In Proceedings of the 24th international conference on Machine learning, pages 807-814. ACM, 2007.
26. Aaron Smith and Joanna Bruenner. Pew research centers internet & american life project: Twitter use 2012. http://pewinternet.org/Reports/2012/ Twitter-Use-2012.aspx, 2012.
27. H. Zou and T. Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301-320, 2005.