Simulating Multivariate Syndromic Time Series and Outbreak Signatures

Abstract

A serious challenge to research in the field of biosurveillance is the lack of available authentic syndromic data to researchers. This significantly limits the possibility of algorithm development and evaluation, and hiders the comparison of methods across different groups of researchers. Since syndromic datasets are usually proprietary and tightly held by their owners, a robust simulation method for multivariate time series derived from syndromic data is required. This paper describes a method for simulating multivariate syndromic count data, in the form of daily counts from multiple syndromic series. The simulator can be used to generate multivariate syndromic data by specifying the requested statistical structure, and as a method to mimic an existing set of syndromic data for the purpose of creating a new dataset with the same statistical properties. The product of this study is both a software program that generates multivariate semi-authentic data, as well as a set of datasets that can serve as an initial repository for researchers. An additional component to the data simulator is an outbreak simulator that generates multivariate signatures of artificial outbreaks of different nature, and which can then be embedded within the simulated data to evaluate detection algorithm performance.