A Comparison of Spatiotemporal Surveillance Methods for Nonhomogeneous Change Size

Sung Won Han, Kyu Jong Lee, Hua Zhong, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Spatiotemporal surveillance, especially in detection of emerging outbreaks is of particular importance. When an outbreak spreads across some areas, the incidence rate at the center of the outbreak area might be expected to be much higher than the rate at its edge. However, to the best of our knowledge, all existing methods assume a uniformly increasing rate across the entire area of the outbreak. The purpose of this study is to compare the performance of the spatiotemporal surveillance methods such as multivariate cumulative sum (MCUSUM) or multivariate exponentially weighted moving average (MEWMA) when the changes in size are nonhomogeneous. Monte Carlo simulations were conducted to examine the properties of these spatiotemporal surveillance methods and compared them in terms of the detection speed and the identification rate under various scenarios. The results showed that when nonhomogeneous change sizes are involved, the MCUSUM method taking into account spatial nonhomogeneity of increase rates yields a better identification than the method ignoring such change size pattern although the detection speeds are similar. Further, a case study for the detection of male thyroid cancer data in New Mexico in the United States was performed to demonstrate the applicability of these methods.

Original languageEnglish
Pages (from-to)2714-2730
Number of pages17
JournalCommunications in Statistics: Simulation and Computation
Volume44
Issue number10
DOIs
Publication statusPublished - 2015 Nov 26

Keywords

  • Change point detection
  • Generalized likelihood ratios
  • Multivariate CUSUM
  • Multivariate EWMA
  • Nonhomogeneous change size
  • Spatiotemporal surveillance

ASJC Scopus subject areas

  • Modelling and Simulation
  • Statistics and Probability

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