摘要：Syndromic data involves data variation that can be difficult to handle by traditional methods of analysis, e.g. mass gatherings, extreme weather and other high-profile events. For the purpose of optimizing baselines for outbreak detection, we carried out a power analysis of data transformations, e.g. ratios and geometric means. ANOVAs were applied to power simulations, using the gamma distribution to generate baseline and outbreak distributions. The results were compared with empirical findings on syndromic surveillance (Swedish Health Care Direct 1177). The study supports the potential value of data transformations to increase detection power and control for sporadic events.
