Article
Dynamics and numerical simulations to predict empirical antibiotic treatment of multi-resistant Pseudomonas aeruginosa infection
Author/s | López de la Cruz, Javier
Pérez-Aranda Redondo, María Alcudia Cruz, Ana Begines Ruiz, Belén Caraballo Garrido, Tomás Pajuelo Domínguez, Eloísa Ginel Pérez, Pedro José |
Department | Universidad de Sevilla. Departamento de Ecuaciones Diferenciales y Análisis Numérico Universidad de Sevilla. Departamento de Química Orgánica y Farmacéutica Universidad de Sevilla. Departamento de Microbiología y Parasitología |
Publication Date | 2020-12 |
Deposit Date | 2020-09-08 |
Published in |
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Abstract | This work discloses an epidemiological mathematical model to predict an empirical treatment for dogs infected by Pseudomonas aeruginosa. This dangerous pathogen is one of the leading causes of multi-resistant infections ... This work discloses an epidemiological mathematical model to predict an empirical treatment for dogs infected by Pseudomonas aeruginosa. This dangerous pathogen is one of the leading causes of multi-resistant infections and can be transmitted from dogs to humans. Numerical simulations and appropriated codes were developed using Matlab software to gather information concerning long-time dynamics of the susceptible, infected and recovered individuals. All data compiled from the mathematical model was used to provide an appropriated antibiotic sensitivity panel for this specific infection. In this study, several variables have been included in this model to predict which treatment should be prescribed in emergency cases, when there is no time to perform an antibiogram or the cost of it could not be assumed. In particular, we highlight the use of this model aiming to become part of the convenient toolbox of Public Health research and decision-making in the design of the mitigation strategy of bacterial pathogens. |
Funding agencies | Ministerio de Educación y Ciencia (MEC). España Ministerio de Ciencia, Innovación y Universidades (MICINN). España Junta de Andalucía. Consejería de Economía y Conocimiento |
Project ID. | 2004/0001203
PGC2018-096540-B-I00 US-1254251 |
Citation | López de la Cruz, J., Pérez-Aranda Redondo, M., Alcudia Cruz, A., Begines Ruiz, B., Caraballo Garrido, T., Pajuelo Domínguez, E. y Ginel Pérez, P.J. (2020). Dynamics and numerical simulations to predict empirical antibiotic treatment of multi-resistant Pseudomonas aeruginosa infection. Communications in Nonlinear Science and Numerical Simulation, 91 (105418), 1-20. |
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