arXiv subjects

Morgane Dumont

Publications and source records attributed to Morgane Dumont.

3 recordsLinked to original sources

arXiv2026

An Age-Structured Vaccination Strategy for Epidemic Containment: A Model Predictive Control Approach

This work presents a novel Model Predictive Control (MPC) approach to develop an optimal age-structured vaccination strategy for the containment of COVID-19 in Wallonia, Belgium. The proposed MPC framework is designed to minimize deaths, achieve early disease eradication, and adhere to operational constraints. By incorporating an age-structured Susceptible-Infected-Recovered-Deceased (SIRD) model with an additional term for vaccination, the MPC strategy dynamically adapts to the evolving epidemic state. A detailed proof of the asymptotic stability and recursive feasibility of the proposed MPC algorithm is provided. This ensures that the optimal cost at each step provides an upper bound on the minimal number obtainable of deaths at the end of the pandemic. Moreover, simulations demonstrate that the proposed MPC approach outperforms the decreasing age vaccination strategy adopted by the Belgian government during the first wave of vaccinations. The results highlight the potential of MPC-based vaccination strategies to reduce the total number of deaths, accelerate disease eradication, and optimize vaccine administration.

math.OC↗

arXiv2025

Uncertainty assessment of spatial dynamic microsimulations

Spatial dynamic microsimulations probabilistically project geographically referenced units with individual characteristics over time. Like any projection method, their outcomes are inherently uncertain and sensitive to multiple factors. However, such factors are rarely addressed. Applying variance-based sensitivity analysis to both direct and indirect effects within the employment module of the MikroSim model for Germany, we show that commonly considered sources of uncertainty, namely coefficient and parameter uncertainty, are less influential than qualitative modeling choices. Because dynamic microsimulations are inherently complex and are computationally intensive, it is crucial to consider potential factors of uncertainty and their influence on simulation outputs in order to more carefully design simulation setups and better communicate results. We find, that simple summary measures insufficiently capture overall model uncertainty and urge modelers to account for these broader sources when designing microsimulations and their results.

stat.CO↗

arXiv2018

Comparison of Discrete Choice Models and Artificial Neural Networks in Presence of Missing Variables

Classification, the process of assigning a label (or class) to an observation given its features, is a common task in many applications. Nonetheless in most real-life applications, the labels can not be fully explained by the observed features. Indeed there can be many factors hidden to the modellers. The unexplained variation is then treated as some random noise which is handled differently depending on the method retained by the practitioner. This work focuses on two simple and widely used supervised classification algorithms: discrete choice models and artificial neural networks in the context of binary classification. Through various numerical experiments involving continuous or discrete explanatory features, we present a comparison of the retained methods' performance in presence of missing variables. The impact of the distribution of the two classes in the training data is also investigated. The outcomes of those experiments highlight the fact that artificial neural networks outperforms the discrete choice models, except when the distribution of the classes in the training data is highly unbalanced. Finally, this work provides some guidelines for choosing the right classifier with respect to the training data.

stat.ML↗