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Computational Methods in Synthetic Biology pp 157–167Cite as

A Deterministic Compartmental Modeling Framework for Disease Transmission

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 2189))

Abstract

Mathematical models for the spread of diseases help us understand the mechanisms on how diseases spread, evaluate the possible effects of interventions, predict outcomes of epidemics, and forecast the course of outbreaks. Compartmental models are widely used in synthetic biology since they can represent a biological system as an assembly of various parts or compartments with different functions. Here we present a framework for the analysis of a compartmental model for the transmission of diseases using ordinary differential equations. We apply this method on a study about the spread of tuberculosis.

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References

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Acknowledgments

AL holds a research fellowship from De La Salle University. The funders had no role in the design of the study, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Mathematics, School of Sciences and Engineering, University of Asia and the Pacific, Pasig City, Philippines

    King James B. Villasin & Eva M. Rodriguez

  2. Mathematics and Statistics Department, De La Salle University, Manila, Philippines

    Angelyn R. Lao

Authors
  1. King James B. Villasin
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  2. Eva M. Rodriguez
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  3. Angelyn R. Lao
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Corresponding author

Correspondence to Eva M. Rodriguez .

Editor information

Editors and Affiliations

  1. School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China

    Mario Andrea Marchisio

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Villasin, K.J.B., Rodriguez, E.M., Lao, A.R. (2021). A Deterministic Compartmental Modeling Framework for Disease Transmission. In: Marchisio, M.A. (eds) Computational Methods in Synthetic Biology. Methods in Molecular Biology, vol 2189. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0822-7_12

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  • DOI: https://doi.org/10.1007/978-1-0716-0822-7_12

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-0821-0

  • Online ISBN: 978-1-0716-0822-7

  • eBook Packages: Springer Protocols

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