Discovering dynamic patterns from infectious disease data using dynamic mode decomposition.
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.
Institute for Disease Modeling Bellevue, WA 98004, USA joshlproctor@gmail.com.
Joshua L
JL
Proctor
Institute for Disease Modeling Bellevue, WA 98004, USA.
Philip A
PA
Eckhoff
D000465:Algorithms; D003141:Communicable Diseases / Q000635:transmission*; D004196:Disease Outbreaks; D006295:Health Resources; D006801:Humans; D007251:Influenza, Human / Q000635:transmission*; D008457:Measles / Q000635:transmission*; D009549:Nigeria; D011051:Poliomyelitis / Q000635:transmission*; D011159:Population Surveillance / Q000379:methods*; D006113:United Kingdom; D014611:Vaccination
Dynamic mode decomposition; Equation-free; Modal decomposition; Model reduction; Spatial-temporal patterns