EpiVECS: exploring spatiotemporal epidemiological data using cluster embedding and interactive visualization.

ABSTRACT: The analysis of data over space and time is a core part of descriptive epidemiology, but the complexity of spatiotemporal data makes this challenging. There is a need for methods that simplify the exploration of such data for tasks such as surveillance and hypothesis generation. In this paper, we use combined clustering and dimensionality reduction methods (hereafter referred to as 'cluster embedding' methods) to spatially visualize patterns in epidemiological time-series data. We compare several cluster embedding techniques to see which performs best along a variety of internal cluster validation metrics. We find that methods based on k-means clustering generally perform better than self-organizing maps on real world epidemiological data, with some minor exceptions. We also introduce EpiV

SUBMITTER: Mason L 

PROVIDER: S-EPMC10692107 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature