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Non-integer order analysis of the impact of diabetes and resistant strains in a model for TB infection
•Study the impact of diabetes and multi-drug resistant strains in a model for tuberculosis (TB) infection in a community.•Compute the reproduction number, R0, of the model.•Analyse its behaviour numerically for variation of epidemiologically relevant parameters.•The order of the fractional derivativ...
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Published in: | Communications in nonlinear science & numerical simulation 2018-08, Vol.61, p.104-126 |
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Main Authors: | , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
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Summary: | •Study the impact of diabetes and multi-drug resistant strains in a model for tuberculosis (TB) infection in a community.•Compute the reproduction number, R0, of the model.•Analyse its behaviour numerically for variation of epidemiologically relevant parameters.•The order of the fractional derivative adds more information on the dynamics of the model.
We study the impact of diabetes and multi-drug resistant strains in a non-integer order model for tuberculosis (TB) infection in a community. We compute the reproduction number, R0, of the model and analyse its behaviour numerically for variation of epidemiologically relevant parameters. Namely, the increased susceptibility to TB due to diabetes, the diabetes recruitment rate, and the increased progression of non-diabetics TB infectious to diabetic TB infectious individuals, due to their active TB status. We have proven the global stability of the disease-free equilibrium for specific conditions, related with exogeneous and endogeneous reinfections, and relapse of recovered individuals. Numerical simulations of the model for the above mentioned parameters confirm the dynamics predicted by the value of R0. For R0 1. The sensitivity indexes of R0 are computed and discussed. The order of the fractional derivative adds more information about the complexity of the dynamics of the proposed model and may help distinguishing dynamical traits in distinct TB patients. |
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ISSN: | 1007-5704 1878-7274 |
DOI: | 10.1016/j.cnsns.2018.01.012 |