Choosing the best algorithm for event detection based on the intend application: A conceptual framework for syndromic surveillance

There is an extensive list of methods available for the early detection of an epidemic signal in syndromic surveillance data. However, there is no commonly accepted classification system for the statistical methods used for event detection in syndromic surveillance. Comparing and choosing appropriate event detection algorithms is an increasingly challenging task. Although lists of selection criteria, and statistical methods used for signal detection have been reported, selection criteria are rarely linked to a specific set of appropriate statistical methods. The paper presents a practical approach for guiding surveillance practitioners to make an informed choice from among the most popular event detection algorithms based on the intended application of the algorithm. We developed selection criteria by mapping the assumptions and performance characteristics of event detection algorithms directly to important characteristics of the time series used in syndromic surveillance. We also considered types of epidemics that may be expected and other characteristics of the surveillance system. These guidelines will provide decisions makers, data analysts, public health practitioners, and researchers with a comprehensive but practical overview of the domain, which may reduce the technical barriers to the development and implementation of syndromic surveillance systems in animal and human health. The classification scheme was restricted to univariate and temporal methods because they are the most commonly used algorithms in syndromic surveillance.