The Community for Technology Leaders
RSS Icon
Issue No.04 - July-Aug. (2013 vol.28)
pp: 96-101
Madhav V. Marathe , Virginia Tech
Naren Ramakrishnan , Virginia Tech
Public health epidemiology aims to understand the spatiotemporal spread of diseases and to develop methods to control such spread. Computational epidemiology has become increasingly multidisciplinary and has led to novel computational methods for understanding and controlling spatiotemporal disease spread. Recent advances focus specifically on modeling, data mining, and inferential and planning questions.
Medical services, Epidemiology, Computational modeling,intelligent systems, public health epidemiology, computational epidemiology, machine learning, data mining
Madhav V. Marathe, Naren Ramakrishnan, "Recent Advances in Computational Epidemiology", IEEE Intelligent Systems, vol.28, no. 4, pp. 96-101, July-Aug. 2013, doi:10.1109/MIS.2013.114
1. W.O. Kermack and A.G. McKendrick, “A Contribution to the Mathematical Theory of Epidemics,” Proc. Royal Soc. London A, vol. 115, no. 772, 1927, pp. 700-721.
2. C.L. Barrett, S. Eubank, and M.V. Marathe, “An Interaction Based Approach to Computational Epidemics,” Proc. Ann. Conf. AAAI, AAAI, 2008, pp. 1590-1593.
3. J. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling, Princeton Univ. Press, 2005.
4. L. Glass and R. Glass, “Social Contact Networks for the Spread of Pandemic Influenza in Children and Teenagers,” BMC Public Health, vol. 8, no. 1, 2008, p. 61; doi:10.1186/1471-2458-8-61.
5. M. Salathé et al., “A High-Resolution Human Contact Network for Infectious Disease Transmission,” Proc. Nat'l Academy Sciences, vol. 107, no. 51, 2010, pp. 22020-22025.
6. J. Stehle et al., “Simulation of an SEIR Infectious Disease Model on the Dynamic Contact Network of Conference Attendees,” BMC Medicine, vol. 9, no. 1, 2011, p. 87; doi:10.1186/1741-7015-9-87.
7. A. Madan et al., “Social Sensing for Epidemiological Behavior Change,” Proc. 12th ACM Int'l Conf. Ubiquitous Computing, ACM, 2010, pp. 291-300.
8. C. Barrett et al., “An Integrated Modeling Environment to Study the Co-Evolution of Networks, Individual Behavior, and Epidemics, AI Magazine, vol. 31, no. 1, 2010, pp. 75-87.
9. C. Barrett et al., “Economic and Social Impact of Influenza Mitigation Strategies by Demographic Class,” Epidemics J., vol. 3, no. 1, 2011, pp. 19-31.
10. H.V. Fineberg and M.E. Wilson, “Epidemic Science in Real Time,” Science, vol. 324, no. 5930, 2009, p. 987; doi:10.1126/science.1176297.
11. J. Brownstein, C. Freifeld, and L. Madoff, “Digital Disease Detection—Harnessing the Web for Public Health Surveillance,” New England J. Medicine, vol. 360, no. 21, 2009, pp. 2153-2157.
12. M. Dredze, “How Social Media Will Change Public Health,” IEEE Intelligent Systems, vol. 27, no. 4, 2012, pp. 81-84.
13. M. Salathé et al., “Digital Epidemiology,” PLoS Computational Biology, vol. 8, no. 7, 2012; doi:10.1371/journal.pcbi.1002616.
14. J. Ginsberg et al., “Detecting Influenza Epidemics Using Search Engine Query Data,” Nature, vol. 457, Feb. 2009, pp. 1012-1014.
15. V. Lampos and N. Cristianini, “Nowcasting Events from the Social Web with Statistical Learning,” ACM Trans. Intelligent Systems and Technology, vol. 3, no. 4, 2012; doi:10.1145/2337542.2337557.
16. A. Lamb, M. Paul, and M. Dredze, “Separating Fact from Fear: Tracking Flu Infections on Twitter,” Proc. North Am. Chapter Assoc. Computational Linguistics Conf., Assoc. Computational Linguistics, 2013, pp. 789-794.
17. M. Paul and M. Dredze, “You Are What You Tweet: Analyzing Twitter for Public Health,” Proc. 5th AAAI Int'l Conf. Weblogs and Social Media, AAAI, 2011, pp. 265-272.
18. M. Salathé and S. Khandelwal, “Assessing Vaccination Sentiments with Online Social Media: Implications for Infectious Disease Dynamics and Control,” PLoS Computational Biology, vol. 7, no. 10, 2011; doi:10.1371/journal.pcbi.1002199.
19. A. Sadilek, H. Kautz, and V. Silenzio, “Modeling Spread of Disease from Social Interactions,” Proc. 6th AAAI Int'l Conf. Weblogs and Social Media, AAAI, 2012; index.php/ICWSM/ICWSM12/ paper/view/44934999.
20. N. Christakis and J.H. Fowler, “Social Network Sensors for Early Detection of Contagious Outbreaks,” PLoS ONE, vol. 5, no. 9, 2010; doi:10.1371/journal.pone.0012948.
21. H. Shao et al., Predicting the Flu before It Happens: Designing Social Network Sensors for Epidemics, tech. report 13-063, Network Dynamics and Simulation Science Laboratory (NDSSL), Virginia Bioinformatics Inst., Virginia Tech, 2013.
22. T. Lengauer and R. Tarjan, “A Fast Algorithm for Finding Dominators in a Flowgraph,” ACM Trans. Programming Languages and Systems, vol. 1, no. 1, 1979, pp. 121-141.
23. A. Apolloni et al., Optimal Vaccine Allocation and Vulnerability, tech. report 10-504, Network Dynamics and Simulation Science Laboratory (NDSSL), Virginia Bioinformatics Inst., Virginia Polytechnic Inst. and State Univ., 2010.
24. M. Becker ed., “The Health Belief Model and Personal Health Behavior,” Health Education Monographs, vol. 2, no. 4, 1974, pp. 324-508.
25. A. Bandura, Social Foundations of Thought and Action: A Social Cognitive Theory, Prentice Hall, 1986.
26. S. Funk, M. Salathé, and V. Jansen, “Modelling the Influence of Human Behaviour on the Spread of Infectious Diseases: A Review,” J. Royal Soc. Interface, vol. 7, no. 50, 2010, pp. 1247-1256.
27. K. Bisset et al., “Indemics: An Interactive Data Intensive Framework for High Performance Epidemic Simulation, Proc. 24th ACM Int'l Conf. Supercomputing, ACM, 2010, pp. 233-242.
28. J. Chen, A. Marathe, and M. Marathe, “Coevolution of Epidemics, Social Networks, and Individual Behavior: A Case Study,” Advances in Social Computing, LNCS 6007, Springer, 2010, pp. 218-227.
4 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool