Markov Decision Processes and Best Responses of Epidemic Models

Saul Diaz-Infante (), David Gonzalez-Sanchez () and Gabriel Salcedo-Varela ()
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Saul Diaz-Infante: CONAHCYT-Universidad de Sonora, Departamento de Matemáticas
David Gonzalez-Sanchez: CONAHCYT-Universidad de Sonora, Departamento de Matemáticas
Gabriel Salcedo-Varela: Universidad de Sonora, Departamento de Matemáticas

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1535-1553 from Springer

Abstract: Abstract This chapter provides an accessible methodology to simulate best responses in stochastic epidemic models. A best response is a numerical rule that assigns, at each time, a feasible intervention for each epidemic state, minimizing its incurred cost and subject to the underlying constraints of the model. The proposed methodology is based on the dynamic programming algorithm. As an example, the tomato yellow leaf curl disease is considered in a stochastic model, which is calibrated with real data and used to illustrate the simulation of best responses.

Keywords: Markov decision processes; Epidemics; Optimal control; Dynamic programming; Optimal policy; Best response; Stochastic differential equations (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-16368-4_37

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