Bayesian estimation of parameters in a SI mathematical model for the transmission dynamics of an infectious disease in Peru

Authors

  • Emma Cambillo-Moyano Departamento de Estadística, Universidad Nacional Mayor de San Marcos, Lima, Perú
  • Ysela Agüero-Palacios Departamento de Estadística, Universidad Nacional Mayor de San Marcos, Lima, Perú.
  • Alicia Riojas-Cañari Departamento de Investigación de Operaciones, Universidad Nacional Mayor de San Marcos, Lima, Perú.
  • Pedro Pesantes-Grados Unidad de Posgrado de la Facultad de Ciencias Matemáticas, Universidad Nacional Mayor de San Marcos, Lima, Perú.
  • Roxana López-Cruz Departamento de Matemática, Universidad Nacional Mayor de San Marcos, Lima, Perú. http://orcid.org/0000-0002-7703-5784

DOI:

https://doi.org/10.17268/sel.mat.2023.01.04

Keywords:

ordinary differential equation, multiple level, stability, SI Model, Montecarlo simulation, Bayesian estimation, MCMC

Abstract

The objective of the research is to estimate the transmission rate of an infection (β) in the SI epidemical model, using Bayesian statistical methods from observed data in Peru. After studying the SI mathematical model and Bayesian statistical inference metho’ds, a Bayesian estimator is proposed to estimate the transmisión rate of an infection in this model and a procedure is proposed to estimate this rate using Montecarlo simulation based on Markov chains - MCMC.

Author Biography

Roxana López-Cruz, Departamento de Matemática, Universidad Nacional Mayor de San Marcos, Lima, Perú.

Mathematics, Ph.D. (Arizona State University-USA)

Full Professor, Mathematics Department

Universidad Nacional Mayor de San Marcos, Lima, Perú

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Published

2023-06-14

How to Cite

Cambillo-Moyano, E., Agüero-Palacios, Y., Riojas-Cañari, A., Pesantes-Grados, P., & López-Cruz, R. (2023). Bayesian estimation of parameters in a SI mathematical model for the transmission dynamics of an infectious disease in Peru. Selecciones Matemáticas, 10(01), 41 - 50. https://doi.org/10.17268/sel.mat.2023.01.04

Issue

Vol. 10 No. 01 (2023): Special Issue

Section

Articles

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