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Mathematical epidemiology in a data-rich world.

Julien Arino1

  • 1Department of Mathematics & Data Science NEXUS, University of Manitoba, Winnipeg, Manitoba, Canada.

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Summary

This study advocates for proactively using background data in mathematical epidemiology models. It details methods for obtaining and integrating this crucial data from open sources to improve model accuracy.

Keywords:
Data acquisitionMathematical epidemiologyOpen data

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Area of Science:

  • Epidemiology
  • Mathematical Modeling

Background:

  • Background data is essential for robust mathematical epidemiology models.
  • Current use of background data is often reactive, limiting model potential.

Purpose of the Study:

  • To advocate for a proactive strategy in acquiring and utilizing background data.
  • To provide practical methods for data acquisition and integration into epidemiological models.

Main Methods:

  • Review of data acquisition strategies, focusing on open data sources.
  • Discussion of techniques for incorporating diverse background data into mathematical models.

Main Results:

  • Demonstration of various mechanisms for obtaining relevant background data.
  • Illustrative examples of integrating this data into epidemiological models.

Conclusions:

  • Proactive incorporation of background data enhances the utility and accuracy of mathematical epidemiology models.
  • Open data sources offer accessible avenues for acquiring valuable background information.