Syndromic surveillance on the victorian chief complaint data set using a hybrid statistical and machine learning technique

Emergency Department Chief Complaints have been used to detect the size and the spread of disease outbreaks in the past. Chief complaints are readily available in digital formats and provide a good data source for syndromic surveillance. This paper reports our findings on the identification of the distribution of a few syndromes over time using the Victorian Syndromic Surveillance (SynSurv) data set. We utilized a machine learning-based Naive Bayes classifier to predict the syndromic group of unseen chief complaints. Then, we analyzed the patterns of the distributions of three syndromes in the Syn-Surv data, specifically the Flu-like Illness, Acute Respiratory, and Diarrhoea syndromes, over sliding windows of time using the EARS C1, C2, and C3 aberrancy detection algorithms. The results of our analyses demonstrate that applying aberrancy algorithms over the variance data between two consecutive weeks reduces the large number of possible disease outbreaks detected using raw frequencies of the syndromic groups in the same time period, resulting in a more feasible approach for practical syndromic surveillance.