Detection of outbreaks from time series data using wavelet transform.

In this paper, we developed a new approach to detection of disease outbreaks based on wavelet transform. It is capable of dealing with two problems found in real-world time series data, namely, negative singularity and long-term trends, which may degrade the performance of current approaches to outbreak detection. To test this approach, we introduced artificail disease outbreaks and negative singularities into a real world dataset and applied it and two other algorithms-autoregressive (AR) and Multi-resolution Wavelet Auto-regressive (MWAR) - to this dataset. We compared the performance of these algorithms in terms of sensitivity, specificity and timeliness. The results showed that our approach had similar sensitivity and specificity and slightly better timeliness compared to the other two algorithms. When we introduced negative singularities, its performance did not degrade as much as the other two algorithms' performance. We conclude that our approach to detection, when compared to traditional approaches, may not be as susceptible to degradation of performance caused by negative singularities.