Statistical Models of Bio-surveillance for Monitoring Disease Outbreak
| Project/Area Number |
16300091
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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 | National Institute of Public Health |
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Principal Investigator |
TANGO Toshiro National Institute of Public Health, Department of Technology Assessment and Biostatistics, Director, 技術評価部, 部長 (70124477)
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| Co-Investigator(Kenkyū-buntansha) |
TAKAHASHI Kunihiko National Institute of Public Health, Department of Technology Assessment and Biostatistics, Researcher, 技術評価部, 研究員 (50323259)
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| Project Period (FY) |
2004 – 2006
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| Project Status |
Completed (Fiscal Year 2006)
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| Budget Amount *help |
¥14,700,000 (Direct Cost: ¥14,700,000)
Fiscal Year 2006: ¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2005: ¥5,000,000 (Direct Cost: ¥5,000,000)
Fiscal Year 2004: ¥7,500,000 (Direct Cost: ¥7,500,000)
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| Keywords | Disease clustering / Likelihood ratio test / Poisson distribution / Spatial epidemiology / Spatial statistics / Spatial scan statistic / ボアッソン分布 |
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| Research Abstract |
The anthrax terrorist attacks in 2001, the severe acute respiratory syndrome (SARS) outbreak, and a fear of pandemic flu have motivated many public health departments to develop early disease outbreak detection systems in the United States. Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. Particularly in light of the perceived threat of bioterrorism, there has been a spate of recent interest in the development of biosurveillance systems that can detect changes in spatial patterns of disease. In this research, we developed four statistical models/methods that can be implemented in the surveillance system. We first proposed a flexibly shaped spatial scan statistic that can detect irregular shaped clusters within relatively small neighborhoods of each region. The performance of the proposed spatial scan statistic is compared to that of Kulldorff's circular spatial scan statistic with Mo
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nte Carlo simulation by considering several circular and noncircular hot-spot cluster models. The proposed spatial scan statistic is shown to have good usual powers plus the ability to detect the noncircular hot-spot clusters more accurately than the circular one. We second we proposed an extended power of the cluster detection tests, which includes the usual power as a special case. Further, we define the profile of the extended power, which can be expected to play an important role in the evaluation and comparison of several cluster detection tests. The proposed extended power and its profile are demonstrated by two tests -- Kulldorff's circular spatial scan statistic (1995,1997) and a flexible spatial scan statistic proposed by Tango and Takahashi(2005). Third, we propose a flexibly shaped space-time scan statistic based on the flexible spatial scan statistic. In particular, we develop a prospective space-time scan statistic for early detection of disease outbreaks. The performance of the proposed space-time scan statistic is compared with that of the cylindrical scan statistic, using the benchmark data provided by Kulldorff et al. In order to compare the performance of space-time scan statistics we propose a space-time power distribution by extending the purely spatial bivariate power distribution. Daily syndromic surveillance data in Massachusetts, USA, are used to illustrate the proposed test statistic and also to compare the performance of the flexible space-time scan statistic with that of the cylindrical one. Finally, a class of tests with quadratic forms for detecting spatial clustering of health events based on case-control point data was proposed. It includes Cuzick and Edwards' test statistic(1990). Although they used the property of asymptotic normality of the test statistic, we showed that such an approximation is generally poor for moderately large sample sizes. Instead, we suggested a central chi-square distribution as a better approximation to the asymptotic distribution of the test statistic. Furthermore, not only to estimate the optimal value of the unknown parameter on the scale of cluster but also to adjust for multiple testing due to repeating the procedure by changing the parameter value, we proposed the minimum of the profile p-value of the test statistic for the parameter as an integrated test statistic. Less
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Report
(4 results)
Research Products
(8 results)
