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Parameter Identification in a Tuberculosis Model for Cameroon

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  • Dany Pascal Moualeu-Ngangue
  • Susanna Röblitz
  • Rainald Ehrig
  • Peter Deuflhard

Abstract

A deterministic model of tuberculosis in Cameroon is designed and analyzed with respect to its transmission dynamics. The model includes lack of access to treatment and weak diagnosis capacity as well as both frequency- and density-dependent transmissions. It is shown that the model is mathematically well-posed and epidemiologically reasonable. Solutions are non-negative and bounded whenever the initial values are non-negative. A sensitivity analysis of model parameters is performed and the most sensitive ones are identified by means of a state-of-the-art Gauss-Newton method. In particular, parameters representing the proportion of individuals having access to medical facilities are seen to have a large impact on the dynamics of the disease. The model predicts that a gradual increase of these parameters could significantly reduce the disease burden on the population within the next 15 years.

Suggested Citation

  • Dany Pascal Moualeu-Ngangue & Susanna Röblitz & Rainald Ehrig & Peter Deuflhard, 2015. "Parameter Identification in a Tuberculosis Model for Cameroon," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
    • Handle: RePEc:plo:pone00:0120607
    DOI: 10.1371/journal.pone.0120607
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    References listed on IDEAS

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    1. Tewa, Jean Jules & Dimi, Jean Luc & Bowong, Samuel, 2009. "Lyapunov functions for a dengue disease transmission model," Chaos, Solitons & Fractals, Elsevier, vol. 39(2), pages 936-941.
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    Cited by:

    1. Nura M. R. Ahmad & Cristina Montañola-Sales & Clara Prats & Mustapha Musa & Daniel López & Josep Casanovas-Garcia, 2018. "Analyzing Policymaking for Tuberculosis Control in Nigeria," Complexity, Hindawi, vol. 2018, pages 1-13, November.
    2. Guangbao Guo & Yue Sun & Xuejun Jiang, 2020. "A partitioned quasi-likelihood for distributed statistical inference," Computational Statistics, Springer, vol. 35(4), pages 1577-1596, December.
    3. Munir, Mohammad & Kausar, Nasreen & Shakil, Mohammad, 2021. "Parameter identification for multiperiodic functions," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

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