Outbreak detection: Combinatorial tests for small samples

疫情检测：小样本组合测试

• 批准号: 7393988
• 负责人: NINA Heidi FEFFERMAN
• 金额: $ 10万
• 依托单位: APPLIED BIOMATHEMATICS, INC.
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-02-01 至 2008-07-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7393988
• 关键词: Accounting; Algorithms; Benchmarking; Cells; Communicable Diseases; Communities; Computational algorithm; Computer software; Computers; Condition; Data; Day Care; Detection; Disease; Disease Clusterings; Disease Outbreaks; Disease Reservoirs; Documentation; Drops; Drug Formulations; Epidemic; Event; General Population; Goals; Health Professional; Hospitals; Incidence; Individual; Lead; Literature; Mediating; Methods; Microcomputers; Monitor; Monte Carlo Method; Numbers; Pattern; Performance; Phase; Population; Prisoner; Prisons; Production; Public Health; Publishing; Purpose; Range; Relative (related person); Research; Running; Sampling; Scanning; Schools; Sensitivity and Specificity; Shipping; Ships; Simulate; Standards of Weights and Measures; Statistical Methods; Surveillance Program; Techniques; Testing; Time; biomathematics; combinatorial; computer code; design; nursing home length of stay; social; statistics; technological innovation; time use; tool; user friendly software; user-friendly

DESCRIPTION (provided by applicant): In delimited populations, such as nursing homes, day care centers, prisons, hospitals, and cruise ships, serious outbreaks of illness generally produce limited absolute numbers of disease incidence. Moreover, these groups are often more susceptible to disease than the general population (e.g. Garibaldi et al. 1981, Nimri 1994, March et al. 2000). Additionally, they can have broad effect on the general population, acting as disease reservoirs and leading to increased overall incidence. However, these limited populations cannot be monitored effectively using traditional statistical techniques due to the sparseness of observed incidence, even under epidemic scenarios. The temporal progression of outbreaks and the social-contact mediated dynamics within these smaller groups instead lend themselves directly to exact combinatorial methods. This project will formulate computational algorithms and develop convenient software that implements nine exact combinatorial statistical tests for real-time use by front-line and drop-in surveillance programs focusing on limited or fixed small populations. These tests include: (1) maximum number of cases, (2) linear discrete scan, (3) the visitors test, (4) range-scan, (5) longest run of empty cells, (6) empty cells, (7) extreme values, (8) binomial maximum, and (9) hypergeometric maximum. These tests will be formulated in terms of space-time units, in the sense of the Ederers-Myers-Mantel test, allowing generalizations that account for changes in population over time and across space, while maintaining exactness of the p-values. Although limited tables for a few of these tests have been published, no general algorithms have heretofore been described for any of these methods. In Phase 1, feasibility will be demonstrated by formulating computational algorithms for four of the nine tests, implementing them in software, and studying their sensitivity, specificity, and time to detection using simulated outbreak data. The performance of the new algorithms will be compared to the results of applying the standard statistical techniques. In Phase 2, computational algorithms will be developed for the remaining exact statistics and all will be implemented in a user-friendly software package. The software will be modular in design, allowing for the incorporation of new methods as they are developed. Additional sensitivity and specificity analyses will be conducted using Monte Carlo methods to generate outbreak scenarios with alternate clustering mechanisms. The results will lead to guidance regarding which methods are best for detecting particular types of outbreaks.

描述（由申请人提供）：在限定的人群中，如疗养院，日托中心，监狱，医院和游轮，严重的疾病爆发通常会产生有限的疾病发病率绝对数字。此外，这些群体往往比一般人群更容易患病（例如Garibaldi等人，1981年; Nimri，1994年; March等人，2000年）。此外，它们可能对一般人群产生广泛影响，作为疾病储存库，导致总体发病率增加。然而，即使在流行病情景下，由于观察到的发病率稀少，这些有限的人群也无法使用传统的统计技术进行有效监测。爆发的时间进程和这些较小群体内的社会接触介导的动态，而不是直接借给自己精确的组合方法。该项目将制定计算算法并开发方便的软件，实现九种精确的组合统计测试，供重点关注有限或固定小人群的一线和介入监测项目实时使用。这些测试包括：（1）最大病例数，（2）线性离散扫描，（3）访问者检验，（4）范围扫描，（5）空单元的最长运行，（6）空单元，（7）极值，（8）二项式最大值，和（9）超几何最大值。这些检验将按照Ederers-Myers-Mantel检验的意义，以时空单位来制定，允许概括说明人口随时间和空间的变化，同时保持p值的准确性。尽管已经公布了这些测试中的一些的有限的表，但是迄今为止还没有描述用于这些方法中的任何一个的通用算法。在第一阶段，将通过为九种测试中的四种制定计算算法，在软件中实现它们，并使用模拟爆发数据研究它们的灵敏度，特异性和检测时间来证明可行性。新算法的性能将比较的结果，应用标准的统计技术。在第二阶段，将为其余的精确统计数据开发计算算法，所有这些都将在一个用户友好的软件包中实施。该软件将采用模块化设计，以便在开发新方法时纳入这些方法。将使用蒙特卡罗方法进行额外的敏感性和特异性分析，以生成具有替代聚类机制的爆发情景。研究结果将为确定哪种方法最适合检测特定类型的疫情提供指导。