A comprehensive probabilistic analysis of approximate SIR‐type epidemiological models via full randomized discrete‐time Markov chain formulation with applications

DOI10.1002/mma.6482zbMath1453.92290OpenAlexW3022528597MaRDI QIDQ5135589

Howida Slama, A. Navarro-Quiles, S. K. El-Labany, Mustafa M. Selim, Juan-Carlos Cortés

Publication date: 23 November 2020

Published in: Mathematical Methods in the Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/mma.6482

zbMATH Keywords

simulationsfirst probability density functionrandom variable transformation techniqueSIR epidemiological modelrandomized discrete-time Markov chains

Mathematics Subject Classification ID

Epidemiology (92D30) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Related Items (6)

Bounding pandemic spread by heat spread ⋮ Optimal aquaculture planning while accounting for the size spectrum ⋮ Folding domain functions (FDF): a random variable transformation technique for the non-invertible case, with applications to RDEs ⋮ Poisson integral type quarantine in a stochastic SIR system ⋮ Comparison of pandemic intervention policies in several building types using heterogeneous population model ⋮ A mixed spectral treatment for the stochastic models with random parameters

This page was built for publication: A comprehensive probabilistic analysis of approximate SIR‐type epidemiological models via full randomized discrete‐time Markov chain formulation with applications