An Introduction to Neural Networks for Uncertainty Quantification in Epidemiological Models

Abel Palafox González (), L. Leticia Ramírez-Ramírez (), Román Zúñiga-Macías (), Sergio Barajas-Oviedo () and Ulises Uriostegui-Legorreta ()
Additional contact information
Abel Palafox González: Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierías
L. Leticia Ramírez-Ramírez: Centro de Investigación en Matemáticas, Jalisco S\N
Román Zúñiga-Macías: Centro de Investigación en Matemáticas, Jalisco S\N
Sergio Barajas-Oviedo: Centro de Investigación en Matemáticas, Jalisco S\N
Ulises Uriostegui-Legorreta: Universidad de Guadalajara, Centro Universitario de Ciencias Exactas e Ingenierías

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1779-1823 from Springer

Abstract: Abstract This chapter advocates for the use of Markov Chain Monte Carlo (MCMC) and neural networks methods for uncertainty quantification in epidemiological inverse problems. Accurate calibration of epidemiological model parameters from contagion data enhances the understanding of the disease dynamics and supports the formulation of scenarios for informed decision-making. These inverse problems have been addressed within the uncertainty quantification framework to characterize the statistical uncertainty of the results. Meanwhile, machine learning methods, particularly artificial neural networks, have been widely adopted across disciplines due to their remarkable capacity to capture complex patterns and uncover underlying relationships in data. Consequently, artificial neural networks are increasingly employed for various purposes, including epidemiological modeling. This chapter presents a review of MCMC methods and introduces neural network implementations, in the context of a classical epidemiological model, describing how they are used for uncertainty quantification purposes. Specific aspects of each method are discussed, along with remarks based on a real-world COVID-19 contagion data scenario.

Keywords: MCMC; t-walk; Emcee; Hamiltonian Monte Carlo; Bayesian Neural Networks; PINN; Stochastic variational inference; SEIR; COVID-19; Uncertainty quantification (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16368-4_58

Ordering information: This item can be ordered from
http://www.springer.com/9783032163684

DOI: 10.1007/978-3-032-16368-4_58

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().