Skip to main content

Detection of High-Risk Zones and Potential Infected Neighbors from Infectious Disease Monitoring Data

  • Conference paper
Database Systems for Advanced Applications (DASFAA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7240))

Included in the following conference series:

Abstract

Detecting the high-risk zones as well as potential infected geographical neighbor is necessary and important to reduce the loss caused by infectious disease. However, it is a challenging work, since the outbreak of infectious disease is uncertain and unclear. Moreover, the detection should be efficient otherwise the best control and prevention time may be missed. To deal with this problem, we propose a geography high-risk zones detection method by capturing the significant change in the infectious disease monitoring data. The main contribution of this paper includes: (1) Analyzing the challenges of the early warning and detection of infectious disease outbreak; (2) Proposing a method to detect the zone that the number of monitoring cases changes significantly; (3) Defining the infection perturbation to describe the infection probability between two zones; (4) Designing an algorithm to measure the infection perturbation of infectious disease between adjacent zones; (5) Performing extensive experiments on both real-world data and synthetic data to demonstrate the effectiveness and efficiency of the proposed methods.

This work was supported by the National Natural Science Foundation of China under grant No. 61103042, the National Research Foundation for the Doctoral Program by the Chinese Ministry of Education under grant No.20100181120029, and the Young Faculty Foundation of Sichuan University under grant No. 2009SCU11030.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Kulldorff, M.: A Spatial Scan Statistic. Communications in Statistics: Theory and Methods 26(6), 1481–1496 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  2. Kulldorff, M., Huang, L., Pickle, L., Duczmal, L.: An Elliptic Spatial Scan Statistic. Statistics in Medicine 25(22), 3929–3943 (2006)

    Article  MathSciNet  Google Scholar 

  3. Takahashi, K., Kulldorff, M., Tango, T., Yih, K.: Flexibly Shaped Space-time Scan Statistic for Disease Outbreak Detection and Monitoring. International Journal of Health Geographics 7, 14 (2008)

    Article  Google Scholar 

  4. Jung, I., Kulldorff, M., Klassen, A.: A Spatial Scan Statistic for Ordinal Data. Statistics in Medicine 26(7), 1594–1607 (2007)

    Article  MathSciNet  Google Scholar 

  5. Huang, L., Kulldorff, M., Gregorio, D.: A Spatial Scan Statistic for Survival Data. Biometrics 63(1), 109–118 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Huang, L., Tiwari, R.C., Zou, Z., Kulldorff, M., Feuer, E.J.: Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data. Journal of the American Statistical Association 104(487), 886–898 (2009)

    Article  MathSciNet  Google Scholar 

  7. Wong, W.K., Moore, A., Cooper, G., Wagner, M.: Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks. In: Proc. the 18th National Conference on Artificial Intelligence (AAAI 2002), pp. 217–223. MIT Press (2002)

    Google Scholar 

  8. Wong, W.K., Moore, A., Cooper, G., Wagner, M.: Bayesian Network Anomaly Pattern Detection for Disease Outbreaks. In: Proc. the 20th International Conference on Machine Learning (ICML 2003), pp. 808–815. AAAI Press (2003)

    Google Scholar 

  9. Wong, W.K., Moore, A., Cooper, G., Wagner, M.: What’s Strange About Recent Events (WSARE): An Algorithm for the Early Detection of Disease Outbreaks. Journal of Machine Learning Research 6, 1961–1998 (2005)

    MathSciNet  MATH  Google Scholar 

  10. Wagner, M.M., Tsui, F.C., Espino, J.U., Dato, V.M., Sittig, D.F., Caruana, R.A., McGinnis, L.F., Deerfield, D.W., Druzdzel, M.J., Fridsma, D.B.: The Emerging Science of Very Early Detection of Disease Outbreaks. Journal of Public Health Manag. Pract. 7(6), 51–59 (2001)

    Google Scholar 

  11. Centers for Disease Control and Prevention. Updated Guidelines for Evaluating Public Health Surveillance Systems: Recommendations from the Guidelines Working Group. MMWR 50(RR-13): 1-35 (2001)

    Google Scholar 

  12. Russell, K.L., Rubenstein, J., Burke, R.L., Vest, K.G., Johns, M.C., Sanchez, J.L., Meyer, W., Fukuda, M.M., Blazes, D.L.: The Global Emerging Infection Surveillance and Response System (GEIS), a U.S. Government Tool for Improved Global Biosurveillance: a review of 2009. BMC Public Health 11(suppl. 2), S2 (2011)

    Article  Google Scholar 

  13. Agresti, A.: An Introduction to Categorical Data Analysis, 2nd edn., New Jersey. Wiley Series in Probability and Statistics (2007)

    Google Scholar 

  14. Kullback, S., Leibler, R.A.: On Information and Sufficiency. The Annals of Mathematical Statistics 22(1), 79–86 (1951)

    Article  MathSciNet  MATH  Google Scholar 

  15. Cover, T., Thomas, J.: Elements of Information Theory. Wiley Series in Telecommunications. John Wiley and Sons, New-York (1991)

    Book  MATH  Google Scholar 

  16. Hershey, J., Olsen, P.: Approximating the Kullback Leibler Divergence between Gaussian Mixture Models. In: Proc. of ICASSP, Honolulu, USA (2007)

    Google Scholar 

  17. Minka, T.: Divergence Measures and Message Passing. Technical Report MSR-TR-2005-173, Microsoft research, Cambridge (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tan, B. et al. (2012). Detection of High-Risk Zones and Potential Infected Neighbors from Infectious Disease Monitoring Data. In: Yu, H., Yu, G., Hsu, W., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29023-7_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-29023-7_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29022-0

  • Online ISBN: 978-3-642-29023-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics