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Discovering dynamic patterns from infectious disease data using dynamic mode decomposition.

Joshua L Proctor1, Philip A Eckhoff2

  • 1Institute for Disease Modeling Bellevue, WA 98004, USA joshlproctor@gmail.com.

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|March 4, 2015
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Summary
This summary is machine-generated.

Dynamic Mode Decomposition (DMD) analyzes infectious disease data to improve surveillance and resource allocation for disease eradication efforts, including polio and measles. This computational tool offers efficient analysis of complex spatial-temporal disease dynamics.

Keywords:
Dynamic mode decompositionEquation-freeModal decompositionModel reductionSpatial-temporal patterns

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Data Science

Background:

  • Quantitative methods are crucial for infectious disease eradication, as seen in polio control efforts.
  • Understanding disease dynamics and planning interventions requires advanced analytical tools.

Purpose of the Study:

  • To demonstrate the utility of Dynamic Mode Decomposition (DMD) for analyzing spatial-temporal infectious disease data.
  • To highlight DMD's potential for enhancing disease surveillance and resource allocation.

Main Methods:

  • Dynamic Mode Decomposition (DMD), a method for identifying coherent spatio-temporal patterns in high-dimensional time-series data.
  • Application of DMD to diverse infectious disease datasets, including Google Flu Trends, measles, and poliomyelitis.

Main Results:

  • DMD effectively identified key dynamics in Google Flu Trends, pre-vaccination measles (UK), and poliomyelitis (Nigeria) data.
  • The study illustrates DMD's practical application in disease surveillance and optimizing resource allocation strategies.

Conclusions:

  • Dynamic Mode Decomposition (DMD) is a powerful tool for analyzing spatial-temporal disease patterns.
  • DMD offers an efficient and effective computational approach for infectious disease research and public health planning.