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Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Tensor decomposition for infectious disease incidence data.

Hannah Korevaar1, C Jessica Metcalf1,2, Bryan T Grenfell1,2,3

  • 1Office of Population Research Princeton University Princeton NY USA.

Methods in Ecology and Evolution
|December 31, 2020
PubMed
Summary
This summary is machine-generated.

Tensor decomposition offers a novel method for analyzing infectious disease time series, revealing patterns in seasonality and transmission dynamics across space and time. This approach helps distinguish between annual and biennial signals, providing insights into disease heterogeneity.

Keywords:
demographydisease dynamicsmeaslessignal processingtensorwavelet

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Area of Science:

  • Epidemiology
  • Mathematical Biology
  • Data Science

Background:

  • Infectious disease transmission exhibits seasonality driven by factors like climate, contact rates, and birth rates.
  • Understanding these periodicities is crucial for predicting and controlling disease outbreaks.
  • Traditional methods may struggle to capture complex spatio-temporal variations in disease dynamics.

Purpose of the Study:

  • To introduce tensor decomposition as a novel method for analyzing oscillatory infectious disease time series.
  • To demonstrate the application of tensor decomposition for uncovering dominant epidemic signals and spatio-temporal variations.
  • To highlight the method's potential for identifying heterogeneity in disease transmission.

Main Methods:

  • Application of tensor decomposition to simulated infectious disease data.
  • Analysis of measles incidence data from England and Wales using tensor decomposition.
  • Distinguishing between annual and biennial epidemic signals and their temporal shifts.

Main Results:

  • Tensor decomposition successfully decomposes simulated oscillatory time series, validating its reliability.
  • Analysis of measles data revealed distinct annual and biennial signals across different locations.
  • The method identified shifts in these seasonal signals over time and variations in signal strength by space and population size.

Conclusions:

  • Tensor decomposition is a powerful tool for uncovering dominant epidemic signals and spatio-temporal variations in disease incidence data.
  • This method can isolate variations in disease seasonality attributed to demographic rates.
  • Tensors offer a concise approach to understanding heterogeneity in disease transmission across large datasets.