Outbreak detection: Combinatorial tests for small samples

描述(由申请人提供)：在限定的人群中，如养老院、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。此外，这些群体往往比普通人群更容易感染疾病(例如，Garibaldi等人)。1981年，Nimri，1994年，3月等人。2000)。此外，它们可能对普通人群产生广泛影响，起到疾病宿主的作用，导致总体发病率增加。然而，由于观察到的发病率稀少，即使在流行情况下，也无法使用传统统计技术有效监测这些有限的人口。疫情的时间进程和社会接触在这些较小群体中的动态直接为精确的组合方法提供了依据。该项目将制定计算算法，并开发方便的软件，执行9个精确的组合统计测试，供重点关注有限或固定小人口的前线和临时监测方案实时使用。这些测试包括：(1)最大案例数，(2)线性离散扫描，(3)访客测试，(4)范围扫描，(5)最长空单元格运行，(6)空单元格，(7)极值，(8)二项式最大值，(9)超几何最大值。这些检验将在Ederers-Myers-Mantel检验的意义上，以时空单位的形式制定，允许进行概括，说明人口随时间和跨空间的变化，同时保持p值的准确性。尽管已经公布了其中几种测试的有限表格，但到目前为止还没有为这些方法中的任何一种描述通用算法。在第一阶段，将通过为九种检测中的四种制定计算算法，在软件中实施它们，并使用模拟爆发数据研究它们的敏感性、特异性和检测时间来证明可行性。新算法的性能将与应用标准统计技术的结果进行比较。在第二阶段，将为剩余的准确统计数据开发计算算法，所有这些都将在一个方便用户的软件包中实施。该软件在设计上将是模块化的，允许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，以产生具有替代群聚机制的暴发情景。这些结果将指导哪些方法最适合检测特定类型的疫情。

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.