Accelerating the discovery of space-time patterns of infectious diseases using parallel computing. (November 2016)

• Record Type: Journal Article
• Authors: Hohl, Alexander; Delmelle, Eric; Tang, Wenwu; Casas, Irene
• Is Part Of: Spatial and spatio-temporal epidemiology. Volume 19(2016)
• Page Start: 10
• Page End: 20
• Publication Date: 2016-11
• Subjects: Dengue fever -- Parallel computing -- Space-time analysis; Epidemiology -- Statistical methods -- Periodicals; Epidemiology -- Periodicals; 614.4072
• DOI: 10.1016/j.sste.2016.05.002
• Languages: English
• ISSNs: 1877-5845

Abstract

Highlights: Parallel computing based on adaptive spatiotemporal domain decomposition strategy. Dramatic improvement in the computational efficiency for detecting space-time clusters of dengue fever cases. Extraction of clusters of dengue fever at very fine scale reveals important disease dynamics. Interactive 3D visualization of space-time clusters. Approach portable to other space-time statistics. Abstract: Infectious diseases have complex transmission cycles, and effective public health responses require the ability to monitor outbreaks in a timely manner. Space-time statistics facilitate the discovery of disease dynamics including rate of spread and seasonal cyclic patterns, but are computationally demanding, especially for datasets of increasing size, diversity and availability. High-performance computing reduces the effort required to identify these patterns, however heterogeneity in the data must be accounted for. We develop an adaptive space-time domain decomposition approach for parallel computation of the space-time kernel density. We apply our methodology to individual reported dengue cases from 2010 to 2011 in the city of Cali, Colombia. The parallel implementation reaches significant speedup compared to sequential counterparts. Density values are visualized in an interactive 3D environment, which facilitates the identification and communication of uneven space-time distribution of disease events. Our framework has the potential to enhance the timely monitoring of infectious diseases.