A Mathematical Model for the Impact of 3HP and Social Programme Implementation on the Incidence and Mortality of Tuberculosis: Study in Brazil

In this paper, we presented a mathematical model for tuberculosis with treatment for latent tuberculosis cases and incorporated social implementations based on the impact they will have on tuberculosis incidence, cure, and recovery. We incorporated two variables containing the accumulated deaths and...

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Bibliographic Details
Published in:Bulletin of mathematical biology 2024-06, Vol.86 (6), p.61-61, Article 61
Main Authors: Delgado Moya, Erick Manuel, Ordoñez, Jose Alejandro, Alves Rubio, Felipe, Niskier Sanchez, Mauro, de Oliveira, Robson Bruniera, Volmir Anderle, Rodrigo, Rasella, Davide
Format: Article
Language:English
Subjects:
Antitubercular Agents - administration & dosage
Bayes Theorem
Bayesian analysis
Brazil - epidemiology
Cell Biology
Computer Simulation
Diagnosis
Fatalities
Humans
Incidence
Isoniazid
Isoniazid - administration & dosage
Latent Tuberculosis - drug therapy
Latent Tuberculosis - epidemiology
Latent Tuberculosis - mortality
Life Sciences
Markov Chains
Mathematical and Computational Biology
Mathematical Concepts
Mathematical models
Mathematics
Mathematics and Statistics
Medical diagnosis
Models, Biological
Monte Carlo Method
Monte Carlo simulation
Mortality
Original Article
Rifampin - administration & dosage
Rifampin - analogs & derivatives
Rifampin - therapeutic use
Tuberculosis
Tuberculosis - drug therapy
Tuberculosis - epidemiology
Tuberculosis - mortality
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Summary:In this paper, we presented a mathematical model for tuberculosis with treatment for latent tuberculosis cases and incorporated social implementations based on the impact they will have on tuberculosis incidence, cure, and recovery. We incorporated two variables containing the accumulated deaths and active cases into the model in order to study the incidence and mortality rate per year with the data reported by the model. Our objective is to study the impact of social program implementations and therapies on latent tuberculosis in particular the use of once-weekly isoniazid-rifapentine for 12 weeks (3HP). The computational experimentation was performed with data from Brazil and for model calibration, we used the Markov Chain Monte Carlo method (MCMC) with a Bayesian approach. We studied the effect of increasing the coverage of social programs, the Bolsa Familia Programme (BFP) and the Family Health Strategy (FHS) and the implementation of the 3HP as a substitution therapy for two rates of diagnosis and treatment of latent at 1% and 5%. Based of the data obtained by the model in the period 2023–2035, the FHS reported better results than BFP in the case of social implementations and 3HP with a higher rate of diagnosis and treatment of latent in the reduction of incidence and mortality rate and in cases and deaths avoided. With the objective of linking the social and biomedical implementations, we constructed two different scenarios with the rate of diagnosis and treatment. We verified with results reported by the model that with the social implementations studied and the 3HP with the highest rate of diagnosis and treatment of latent, the best results were obtained in comparison with the other independent and joint implementations. A reduction of the incidence by 36.54% with respect to the model with the current strategies and coverage was achieved, and a greater number of cases and deaths from tuberculosis was avoided.
ISSN:0092-8240
1522-9602
DOI:10.1007/s11538-024-01285-1