419
Views
2
CrossRef citations to date
0
Altmetric
Original Article

Managing uncertainty in early estimation of epidemic behaviors using scenario trees

, , , &
Pages 828-842 | Received 01 Jun 2012, Accepted 01 Apr 2013, Published online: 01 May 2014

References

  • Allen, L. and Burgin, A. (2000) Comparison of deterministic and stochastic Sis and Sir models in discrete time. Mathematical Biosciences, 163(1), 1–34.
  • Anderson, R.M. and May, R.M. (1979) Population biology of infectious diseases: part 1. Nature, 280, 361–367.
  • Bettencourt, L. and Ribeiro, R. (2008) Real time Bayesian estimation of the epidemic potential of emerging infectious diseases. PLoS One, 3(5), e2185.
  • Brown, C. and Davis, H. (2006) Receiver operating characteristics curves and related decision measures: a tutorial. Chemometrics and Intelligent Laboratory Systems, 80(1), 24–38.
  • Buckeridge, D., Burkom, H., Moore, A., Pavlin, J., Cutchis, P. and Hogan, W. (2004) Evaluation of syndromic surveillance systems: design of an epidemic simulation model. MMWR Morbidity Mortality Weekly Report, 53, 137–143.
  • Carrat, F., Luong, J., Lao, H., Sallé, A., Lajaunie, C. and Wackernagel, H. (2006) A “small-world-like” model for comparing interventions aimed at preventing and controlling influenza pandemics. BMC Medicine, 4, 26.
  • Cazelles, B. and Chau, N. (1997) Using the kalman filter and dynamic models to assess the changing HIV/AIDS epidemic. Mathematical Biosciences, 140(2), 131–154.
  • Chen, H., Zeng, D. and Yan, P. (2009) Infectious Disease Informatics: Syndromic Surveillance for Public Health and Biodefense, volume 21,. Springer, New York, NY.
  • Cooper, G., Dash, D., Levander, J., Wong, W., Hogan, W. and Wagner, M. (2004) Bayesian biosurveillance of disease outbreaks, in Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence, AUAI Press, Arlington, VA, pp. 94–103.
  • Dailey, L., Watkins, R. and Plant, A. (2007) Timeliness of data sources used for influenza surveillance. Journal of the American Medical Informatics Association, 14(5), 626–631.
  • Daley, D. and Gani, J. (1996) Epidemic Modelling, Cambridge University Press, Cambridge, UK.
  • Dangerfield, C.E., Ross, J.V. and Keeling, M.J. (2009) Integrating stochasticity and network structure into an epidemic model. Journal of Royal Society Interface, 6(38), 761–774.
  • Di Domenica, N., Mitra, G., Valente, P. and Birbilis, G. (2007) Stochastic programming and scenario generation within a simulation framework: an information systems perspective. Decision Support Systems, 42(4), 2197–2218.
  • Dupačová, J., Consigli, G. and Wallace, S. (2000) Scenarios for multistage stochastic programs. Annals of Operations Research, 100(1), 25–53.
  • Friedman, J. and Fisher, N. (1999) Bump hunting in high-dimensional data. Statistics and Computing, 9(2), 123–143.
  • Ginsberg, J., Mohebbi, M., Patel, R., Brammer, L., Smolinski, M. and Brilliant, L. (2008) Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014.
  • Henrion, R., Küchler, C. and Römisch, W. (2009) Scenario reduction in stochastic programming with respect to discrepancy distances. Computational Optimization and Applications, 43(1), 67–93.
  • Hood, G., Barry, S. and Martin, P. (2009) Alternative methods for computing the sensitivity of complex surveillance systems. Risk Analysis, 29(12), 1686–1698.
  • Høyland, K. and Wallace, S. (2001) Generating scenario trees for multistage decision problems. Management Science, 47(2), 295–307.
  • HSPD-21., (2007) Homeland security presidential directive. Available at http://www.fas.org/irp/offdocs/nspd/hspd-21.htm.
  • Hulth, A., Rydevik, G. and Linde, A. (2009) Web queries as a source for syndromic surveillance. PLoS One, 4(2), e4378 (accessed May 8, 2012).
  • Hurt-Mullen, K. and Coberly, J. (2005) Syndromic surveillance on the epidemiologist’s desktop: making sense of much data. MMWR Morbidity and Mortality Weekly Report, 54, 141–146.
  • Kaut, M. and Wallace, S. (2007) Evaluation of scenario-generation methods for stochastic programming. Pacific Journal of Optimization, 3(2), 257–271.
  • Lloyd, A. (2001) Realistic distributions of infectious periods in epidemic models: changing patterns of persistence and dynamics. Theoretical Population Biology, 60(1), 59–71.
  • Lotze, T. and Shmueli, G. (2008) Ensemble forecasting for disease outbreak detection, in Proceedings of the 23rd AAAI Conference on Artficial Intelligence, Fox, D. and Gomes, C.P. (eds), AAAI Press, Palo Alto, CA, pp. 1470–1471.
  • Lotze, T., Shmueli, G. and Yahav, I. (2009) Simulating and evaluating biosurveillance datasets, in Biosurveillance: Methods and Case Studies, Kass-Hout, T. and Zhang, X. (eds), Chapman & Hall/CRC, Boca Raton, FL, pp. 23–52.
  • Maciejewski, R., Hafen, R., Rudolph, S., Tebbetts, G., Cleveland, W., Grannis, S. and Ebert, D. (2009) Generating synthetic syndromic-surveillance data for evaluating visual-analytics techniques. IEEE Computer Graphics and Applications, 29(3), 18–28.
  • Mondini, A., de Moraes Bronzoni, R., Nunes, S., Neto, F., Massad, E., Alonso, W., Lázzaro, E., Ferraz, A., de Andrade Zanotto, P. and Nogueira, M. (2009) Spatio-temporal tracking and phylodynamics of an urban dengue 3 outbreak in Sao Paulo, Brazil. PLoS Neglected Tropical Diseases, 3(5), e448.
  • Novozhilov, A.S. (2008) On the spread of epidemics in a closed heterogeneous population. Mathematical Biosciences, 215(2), 177–185.
  • Paul, M., Held, L. and Toschke, A. (2008) Multivariate modelling of infectious disease surveillance data. Statistics in Medicine, 27(29), 6250–6267.
  • Pollett, P., Dooley, A. and Ross, J. (2010) Modelling population processes with random initial conditions. Mathematical Biosciences, 223(2), 142–150.
  • Ristic, B., Skvortsov, A. and Morelande, M. (2009) Predicting the progress and the peak of an epidemic, in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Press, Piscataway, NJ, pp. 513–516.
  • Roy, M. and Pascual, M. (2006) On representing network heterogeneities in the incidence rate of simple epidemic models. Journal of Ecological Complexity, 3(1), 80–90.
  • Schindeler, S., Muscatello, D., Ferson, M., Rogers, K., Grant, P. and Churches, T. (2009) Evaluation of alternative respiratory syndromes for specific syndromic surveillance of influenza and respiratory syncytial virus: a time series analysis. BMC Infectious Diseases, 9(1), 190.
  • Skvortsov, A. and Ristic, B. (2012) Monitoring and prediction of an epidemic outbreak using syndromic observations. Mathematical Biosciences, 240, 12–19.
  • Skvortsov, A., Ristic, B. and Woodruff, C. (2010) Predicting an epidemic based on syndromic surveillance, in Proceedings of the 13th Conference on Information Fusion, IEEE Press, Piscataway, NJ, pp. 1–8.
  • Sparks, R., Keighley, T. and Muscatello, D. (2010) Early warning CUSUM plans for surveillance of negative binomial daily disease counts. Journal of Applied Statistics, 37(11), 1911–1929.
  • Sparks, R., Okugami, C. and Bolt, S. (2012) Outbreak detection of spatio-temporally smoothed crashes. Open Journal of Safety Science and Technology, 2(3), 98–107.
  • Van Herwaarden, O.A. and Grasman, J. (1995) Stochastic epidemics: major outbreaks and the duration of the endemic period. Journal of Mathematical Biology, 33(4), 581–601.
  • Wagner, M., Moore, A. and Aryel, R. (2011) Handbook of Biosurveillance. Elsevier Academic Press, Burlington, MA.
  • Wilson, A., Wilson, G. and Olwell, D. (2006) Statistical Methods in Counterterrorism: Game Theory, Modeling, Syndromic Surveillance, and Biometric Authentication. Springer, New York, NY.
  • Wilson, N., Mason, K., Tobias, M., Peacey, M., Huang, Q. and Baker, M. (2009) Interpreting Google flu trends data for pandemic H1N1 influenza: the New Zealand experience. Euro Surveillance: European Communicable Disease Bulletin, 14(44), 429–433.
  • Zheng, W., Aitken, R., Muscatello, D. and Churches, T. (2007) Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments. BMC Public Health, 7(1), 250.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.