Journal article
Accelerating the discovery of space-time patterns of infectious diseases using parallel computing
Spatial and spatio-temporal epidemiology, v 19, pp 10-20
01 Nov 2016
PMID: 27839573
Featured in Collection : UN Sustainable Development Goals @ Drexel
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. (C) 2016 Elsevier Ltd. All rights reserved.
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Details
- Title
- Accelerating the discovery of space-time patterns of infectious diseases using parallel computing
- Creators
- Alexander Hohl - University of North Carolina at CharlotteEric Delmelle - University of North Carolina at CharlotteWenwu Tang - University of North Carolina at CharlotteIrene Casas - Louisiana Tech University
- Publication Details
- Spatial and spatio-temporal epidemiology, v 19, pp 10-20
- Publisher
- Elsevier
- Number of pages
- 11
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Urban Health Collaborative
- Web of Science ID
- WOS:000409345000003
- Scopus ID
- 2-s2.0-84971602790
- Other Identifier
- 991021874419004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Public, Environmental & Occupational Health