Multiscale decomposition of spatial lattice data for hotspot prediction

Abstract

Being able to identify areas with potential risk of becoming a hotspot of disease cases is important for decision makers. This is especially true in the case such as the recent COVID-19 pandemic where it was needed to incorporate prevention strategies to restrain the spread of the disease. In this thesis, we first extend the Discrete Pulse Transform (DPT) theory for irregular lattice data as well as consider its efficient implementation, the Roadmaker's Pavage algorithm (RMPA), and visualisation. The DPT was derived considering all possible connectivities satisfying the morphological definition of connection. Our implementation allows for any connectivity applicable for regular and irregular lattices. Next, we make use of the DPT to decompose spatial lattice data along with the multiscale Ht-index and the spatial scan statistic as a measure of saliency on the extracted pulses to detect significant hotspots.

In the literature, geostatistical techniques such as Kriging has been used in epidemiology to interpolate disease cases from areal data to a continuous surface. Herein, we extend the estimation of a variogram to spatial lattice data. In order to increase the number of data points from only the centroids of each spatial unit (representative points), multiple points are simulated in an appropriate way to represent the continuous nature of the true underlying event occurrences more closely. We thus represent spatial lattice data accurately by a continuous spatial process in order to capture the spatial variability using a variogram.

Lastly, we incorporate the geographically and temporally weighted regression spatio-temporal Kriging (GTWR-STK) method to forecast COVID-19 cases to a next time step. The GTWR-STK method is applied to spatial lattice data where the spatio-temporal variogram is estimated by extending the proposed variogram for spatial lattice data. Hotspots are predicted by applying the proposed hotspot detection method to the forecasted cases.