An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data

Abstract

Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies. The state-of-the-art method for this kind of problem is the Space-time Scan Statistics (SaTScan) which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson or Gaussian counts. Addressing this problem, an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution. However, it is based on the population counts data which are not always available in the least developed countries. In addition, the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales, where the catchment area for each hospital/pharmacy is undefined. We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts. The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts. The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods: Multi-EigenSpot and SaTScan. The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.