The analysis of local sensibility of a mathematical model on antibiotic resistance
El análisis de sensibilidad local de un modelo matemático sobre resistencia antibiótica
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The analysis of local sensitivity (ASL) is a rarely used method, but it is important when deciding which parameters within a model have the greatest influence or effect within it, it even allows the suppression of certain parameters whose sensitivity index is almost zero. In this research work the model of bacteria sensitive and resistant to the antibiotic of Esteva et al. (2011). To which ASL will be performed by means of the Turányi Normalized Local Sensitivity Coefficients method. The ASL reveals that the reproduction rate of sensitive and resistant bacteria are the factors that have the most influence within the proposed model.
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Castellanos, J. Ibargüen, E. y Romero, J. (2019). An optimal control problem and cost-effectiveness analysis of malaria disease with vertical transmission applied to San Andrés de Tumaco (Colombia). Sociedade Brasileira de Matemática Aplicada e Computacional, 8 (133), 1-24
Dye, C. y Williams, B.G. (2000). Criteria for the control of drug-resistant tuberculosis. Proceedings of National Academy of Sciences. 97(14), 8180-8185
Dye, Ch. y Espinal M. A. (2001). Will tuberculosis become resistant to all antibiotics?. Proceedings of the Royal Society B: Biological Sciences 268, 45-52
Esteva, L. y Ibarguen, E. (2018). Modeling basic aspects of bacterial resistance of Mycobacterium tuberculosis to antibiotics. Ricerche mat. 67, 69–88. https://doi.org/10.1007/s11587-017-0347-7
Esteva, L. Ibargüen, E. y Romero, J. (2011). Un modelo matemático sobre bacterias sensibles y resistentes a antibióticos. Matemáticas: Enseñanza Universitaria, 15 (2), 55-73
Gong, M. Yang, Z. Samtem, B. Cave, M. y Barnes, P. (1999) Enhanced capacity of a widespread strain of Mycobacterium tuberculosis to grow in human macrophages. The Journal of Infectious Diseases. 179(5), 1213-1217
Hoal-Van Helden, E.G. Hon, D. Lewis, L. Beyers, N. y Van Helden P.D. (2001) Mycobacterial growth in human macrophages: variation according to donor, inoculum and bacterial strain. Cell Biol. Int. 25(1), 71-81
Hu Y, Pertinez H, Ortega. F, Alameda. L, Liu Y., Schipani A., Davies G., Coates A. (2016). Investigation of Elimination Rate, Persistent Subpopulation Removal, and Relapse Rates of Mycobacterium tuberculosis by Using Combinations of First-Line Drugs in a Modi_ed Cornell Mouse Model. Antimicrob Agents Chemother, 60 (8). 4778-4785
Jurgen, L. Davidenko, N. y Jacques, R. (2013) Análisis de sensibilidad de las constantes cinéticas como herramienta para la elucidación del mecanismo de polimerización de compuestos acrilfuránicos. Avances en Ciencias e Ingeniería 4 (4), 47-63
Kirch, J. Thomaseth, C. Jensch, A. y Radde, N. (2016), The effect of model recaling and normalization on sensitivity analysis on an example of a MAPK pathway model EPJ Nonlinear Biomedical Physics. 4(3), 1-24. https://doi.org/10.1140/epjnbp/s40366-016-0030-z
Lange, J. Davidenko, N. y Rieumont, J. (2013). Análisis de sensibilidad de las constantes cinéticas como herramienta para la elucidación del mecanismo de polimerización de compuestos acrilfuránicos. Avances en Ciencias e Ingeniería, 4(4), Executive Business School. La Serena, Chile. 47-63
Li, Q. Whalen, C.C. Albert, J.M. Larkin, R. Zukowsy, L. Cave, M.D. y Silver, R.F. (2002). Differences in rate and variability of intracellular growth of a panel of Mycobacterium tuberculosis clinical isolates within monocyte model. Infection and Immunity.70(11), 6489-6493
Link, K. Stobb, M. Paola, J. Neeves, K. Fogelson, A. Sindi, S. y Leideman, K. (2018). A local and global sensitivity analysis of a mathematical model of coagulation and platelet deposition under flow. https://doi.org/10.1371/journal.pone.0200917
López, I. Ramirez, A. y Rojano,A. (2004) análisis de sensibilidad de un modelo dinámico de crecimiento para lechugas (Lactuca sativa L.) cultivadas en invernadero. Agrociencia, 38(6), 613-624
Ozcaglar. C, Shabbeer. A, Vandenberg. S, Yener. B, Bennett, K.P, Zhang. Y, Dhandayuthapani. S, y Deretic V. (2012) Epidemiological models of Mycobacterium tuberculosis complex infections. Math Biosci. 236 (2), 77-96
Pacheco, N (2016). La motivación y las matemáticas. Eco Matemático, 7(1), 149–158. https://doi.org/10.22463/17948231.1026
Restrepo. Y, Bermudez, J. R. (2010). Implementación del modelo matemático para el sistema de control de una incubadora para aves utilizando la herramienta computacional MATLAB® - SIMULINK®. Eco Matemático, 1(1), 30–35. https://doi.org/10.22463/17948231.220
Turányi, T. (1997). Applications of sensitivity analysis to combustion chemistry, Reliability Engineering and System Safety, 57, 41-48
