期刊
INTERNATIONAL HEALTH
卷 7, 期 2, 页码 139-145出版社
OXFORD UNIV PRESS
DOI: 10.1093/inthealth/ihv009
关键词
Dynamic mode decomposition; Equation-free; Modal decomposition; Model reduction; Spatial-temporal patterns
资金
- Global Good Fund, Bellevue, WA, USA
Background: The development and application of quantitative methods to understand disease dynamics and plan interventions is becoming increasingly important in the push toward eradication of human infectious diseases, exemplified by the ongoing effort to stop the spread of poliomyelitis. Methods: Dynamic mode decomposition (DMD) is a recently developed method focused on discovering coherent spatial-temporal modes in high-dimensional data collected from complex systems with time dynamics. The algorithm has a number of advantages including a rigorous connection to the analysis of nonlinear systems, an equation-free architecture, and the ability to efficiently handle high-dimensional data. Results: We demonstrate the method on three different infectious disease sets including Google Flu Trends data, pre-vaccination measles in the UK, and paralytic poliomyelitis wild type-1 cases in Nigeria. For each case, we describe the utility of the method for surveillance and resource allocation. Conclusions: We demonstrate how DMD can aid in the analysis of spatial-temporal disease data. DMD is poised to be an effective and efficient computational analysis tool for the study of infectious disease.
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