arXiv subjects

Romario Gildas Foko Tiomela

Publications and source records attributed to Romario Gildas Foko Tiomela.

3 recordsLinked to original sources

arXiv2025

Stationary and Non-Stationary Transition Probabilities in Decision Making: Modeling COVID-19 Dynamics

This study introduces a comparative modeling framework using stationary and non-stationary transition probabilities within a Markov Decision Process (MDP) to assess COVID-19 disease dynamics. Stationary transition probabilities assume constant transition rates, while non-stationary transitions reflect time-dependent behaviors including policy interventions or behavioral changes. We develop a comprehensive compartmental model with transitions based on binomial and multinomial processes. Mathematical models for both stationary and non-stationary transition frameworks are developed and simulated over a 365-day period to emphasize dynamic variations in epidemic outcomes. Our findings highlight the significance of non-stationary modeling in accurately representing the dynamic characteristics of pandemic situations and provide recommendations for optimizing public health interventions under uncertainty. This comparative analysis offers useful information for epidemiological modeling and decision-making in dynamic risk environments.

physics.soc-ph↗

arXiv2025

Optimal Control of Covid-19 Interventions in Public Health Management

This study explores the application of Pontryagin's Maximum Principle to derive optimal strategies for controlling the spread of COVID-19, leveraging a novel compartmental model to capture the disease dynamics. We prioritize three key criteria: cost, effectiveness, and feasibility, each examined independently to evaluate their unique contributions to pandemic management. By addressing these criteria, this study aims to design intervention strategies that are scientifically robust, practical, and economically sustainable. Furthermore, the focus on cost, effectiveness and feasibility seeks to provide policymakers with actionable insights for implementing interventions that maximize public health benefits while remaining feasible under real-world conditions.

math.OC↗

arXiv2022

Stackelberg strategy on a degenerate parabolic equation with missing data

This paper deals with the hierarchic control of a degenerate parabolic equation with missing initial condition. We present a Stackelberg strategy combining the concept of null controllability with low-regret control. We assume that we can act on the system through a set of hierarchic controls. The main control called the leader is in charge of the null controllability while the second control named the follower solves an optimal control problem involving a missing data. The main novelty of this work is the derivation of a new Carleman inequality for a degenerate system, which is used in a standard way to show observability inequality of the adjoint degenerate systems.

math.OC↗