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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: Dy...

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Detalles Bibliográficos
Autores principales: Proctor, Joshua L., Eckhoff, Philip A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4379984/
https://www.ncbi.nlm.nih.gov/pubmed/25733564
http://dx.doi.org/10.1093/inthealth/ihv009
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author Proctor, Joshua L.
Eckhoff, Philip A.
author_facet Proctor, Joshua L.
Eckhoff, Philip A.
author_sort Proctor, Joshua L.
collection PubMed
description 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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spelling pubmed-43799842015-08-07 Discovering dynamic patterns from infectious disease data using dynamic mode decomposition Proctor, Joshua L. Eckhoff, Philip A. Int Health Original Articles 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. Oxford University Press 2015-03 2015-02-26 /pmc/articles/PMC4379984/ /pubmed/25733564 http://dx.doi.org/10.1093/inthealth/ihv009 Text en © The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Articles
Proctor, Joshua L.
Eckhoff, Philip A.
Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
title Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
title_full Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
title_fullStr Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
title_full_unstemmed Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
title_short Discovering dynamic patterns from infectious disease data using dynamic mode decomposition
title_sort discovering dynamic patterns from infectious disease data using dynamic mode decomposition
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4379984/
https://www.ncbi.nlm.nih.gov/pubmed/25733564
http://dx.doi.org/10.1093/inthealth/ihv009
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