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Adaptive likelihood ratio approaches for the detection of space–time disease clusters

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  • de Lima, Max Sousa
  • Duczmal, Luiz Henrique

Abstract

A methodology based on adaptive likelihood ratios (ALRs) for the detection of emerging disease clusters is presented. The martingale structure of the regular likelihood ratio is preserved by the ALR. The upper limit for the false alarm rate of the proposed method depends only on the quantity of evaluated cluster candidates. Thus Monte Carlo simulations are not required to validate the procedures’ statistical significance, allowing the construction of a fast computational algorithm to detect clusters. The number of evaluated clusters is also significantly reduced, through the use of an adaptive approach to prune many unpromising clusters. This further increases the computational speed. Performance is evaluated through simulations to measure the average detection delay and the probability of correct cluster detection. We present applications for thyroid cancer in New Mexico and hanseniasis in children in the Brazilian Amazon.

Suggested Citation

  • de Lima, Max Sousa & Duczmal, Luiz Henrique, 2014. "Adaptive likelihood ratio approaches for the detection of space–time disease clusters," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 352-370.
    • Handle: RePEc:eee:csdana:v:77:y:2014:i:c:p:352-370
    DOI: 10.1016/j.csda.2014.03.015
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    References listed on IDEAS

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    1. Toshiro Tango & Kunihiko Takahashi & Kazuaki Kohriyama, 2011. "A Space–Time Scan Statistic for Detecting Emerging Outbreaks," Biometrics, The International Biometric Society, vol. 67(1), pages 106-115, March.
    2. Daniel B. Neill, 2012. "Fast subset scan for spatial pattern detection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(2), pages 337-360, March.
    3. Krieger A.M. & Pollak M. & Yakir B., 2003. "Surveillance of a Simple Linear Regression," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 456-469, January.
    4. Neill, Daniel B., 2009. "Expectation-based scan statistics for monitoring spatial time series data," International Journal of Forecasting, Elsevier, vol. 25(3), pages 498-517, July.
    5. Peter A. Rogerson, 2001. "Monitoring point patterns for the development of space–time clusters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 87-96.
    6. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.
    7. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    8. Duczmal, Luiz & Cancado, Andre L.F. & Takahashi, Ricardo H.C. & Bessegato, Lupercio F., 2007. "A genetic algorithm for irregularly shaped spatial scan statistics," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 43-52, September.
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