2013 Fiscal Year Final Research Report
Statistical models for detecting emerging outbreaks for health risk monitoring
| Project/Area Number |
23300107
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Showa Women's University |
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Principal Investigator |
TANGO Toshiro 昭和女子大学, 生活機構研究科, 客員教授 (70124477)
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| Co-Investigator(Renkei-kenkyūsha) |
TAKAHASHI Kunihiko 名古屋大学, 医学系研究科, 准教授 (50323259)
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| Project Period (FY) |
2011-04-01 – 2014-03-31
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| Keywords | 疾病集積性 / 尤度比検定 / ポアッソン分布 / バイオテロリズム |
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| Research Abstract |
The circular spatial scan statistic proposed by Kulldorff (1997) has been utilized to detect emerging outbreaks or clusters in many syndromic surveillance systems in USA. However, it cannot detect noncircular, irregularly shaped clusters. The flexible spatial scan statistic proposed by Tango and Takahashi (2005) has also been used for detecting irregularly shaped clusters. However, this method sets a feasible limitation of a maximum of 30 nearest neighbors for searching candidate clusters because of heavy computational load. In this study, we show a flexible spatial scan statistic implemented with a restricted likelihood ratio proposed by Tango (2008) to (1) eliminate the limitation of 30 nearest neighbors and (2) to have surprisingly much less computational time than the original flexible spatial scan statistic. As a side effect, it is shown to be able to detect clusters with any shape reasonably well as the relative risk of the cluster becomes large via Monte Carlo simulation.
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