Outbreak detection: Combinatorial tests for small samples

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 grou ps 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 over all 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-cont act 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 tes ts 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, feasibi lity 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 algori thms 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 wi ll 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. T he results will lead to guidance regarding which methods are best for detecting particular types of outbreaks.