arXiv subjects

Dominique Persano Adorno

Publications and source records attributed to Dominique Persano Adorno.

10 recordsLinked to original sources

arXiv2026

Optimal Control of Pandemic Dynamics via Model Predictive Control: A Health-Economic Trade-off Analysis

This paper addresses the optimal control of epidemic dynamics under conflicting socio-economic objectives. We propose an economic Model Predictive Control (MPC) framework, applied to an extended SEIR-V (Susceptible-Exposed-Infected-Recovered-Vaccinated) compartmental model to govern the spread of an infectious disease while minimizing economic disruption. The control problem is formulated as a constrained nonlinear optimization problem, in which the controller dynamically adjusts social interaction levels (transmission rate beta) and vaccination efforts to minimize a composite cost function that penalizes fatalities, healthcare capacity violations, and economic losses. We conduct a rigorous sensitivity analysis of the prediction horizon N, demonstrating that the closed loop is robust to the horizon choice and that N = 35 days minimizes the realized cost. Furthermore, both the closed-loop solution and an open-loop turnpike analysis across diverse initial conditions reveal that the celebrated "Hammer and Dance" mitigation strategy emerges naturally as the mathematical optimum: the optimal trajectories anchor to a unique suppression turnpike (maximum lockdown) to drive hospitalizations toward the disease-free equilibrium before progressively reopening the economy. Through a turnpike-based argument we establish practical asymptotic stability of the optimal operating point, providing a mathematically grounded decision-support tool for pandemic policy.

math.OC↗

arXiv2026

Extended Kalman Filter-Based State Estimation for a Nine-Compartment Nonlinear Epidemic Model -- Convergence Analysis and In-Silico Benchmark Calibrated on the COVID-19 Third Wave in Italy

This paper addresses real-time state estimation for a nine-compartment nonlinear COVID-19 epidemic model with two co-circulating strains, a super-spreader subpopulation, vaccination with waning immunity, hospitalization, and mortality. Time-varying transmission and vaccination rates are known inputs from a companion calibration, leaving the reconstruction of all nine states from three routinely reported observables: hospitalizations H, fatalities F, and vaccinated stock V. The contributions are theoretical rather than in the filter recursion. First, a Lie-derivative observability analysis yields, via a six-step derivation, the closed-form determinant |det(O9)| = delta_w * gamma_a^2 * kappa * rho2 * w1^2 * (delta_i - delta_p)^2 * |r1 - r2|, showing the level-2 codistribution is rank-deficient at the calibrated symmetric parameters (delta_i = delta_p, r1 = r2); the third Lie derivative restores full rank 9, with r2 the symmetry-breaking parameter. Second, an EKF is designed on the Euler-discretized dynamics with a closed-form 9x9 Jacobian and Joseph covariance update. Third, local exponential mean-square boundedness of the error is proved as a full theorem via the Reif-Gunther-Yaz-Unbehauen hypotheses, exploiting the bilinear drift and linear output to obtain a global-radius quadratic remainder bound that extends to bilinear-drift, linear-output systems. Fourth, the noise covariances are designed from calibration residuals and assessed by NEES and innovation-whiteness tests. All experiments use synthetic measurements from the calibrated model, so reported RMSE values (0.07%-2.72%) are methodology benchmarks, not predictive accuracy. A parameter-mismatch study shows measured and directly-coupled channels stay accurate under model error up to +/-30% while indirectly observed states degrade gracefully. The framework provides the state-feedback basis for future Model Predictive Control.

math.OC↗

arXiv2026

A Nine-Compartment Nonlinear Epidemic Model with Spline-Based Identification of Time-Varying Transmission and Vaccination Dynamics: Application to the COVID-19 Third Wave in Italy

We develop a nine-compartment nonlinear epidemic model incorporating two co-circulating viral strains (ancestral I1 and the Alpha variant B.1.1.7 I2, which is 43-90% more transmissible, c2=1.5), a super-spreader subpopulation, partial vaccine-induced immunity with waning, and explicit hospitalization dynamics with differentiated mortality. Transmission and vaccination rates are treated as time-varying control inputs and identified from Italian COVID-19 data (January-May 2021) via a Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) control-node parameterization, reducing calibration to a fourteen-variable Sequential Quadratic Programming (SQP) problem with monotonicity and box constraints. A parametric bootstrap (n=1000) quantifies parameter uncertainty. The calibrated model achieves R^2=0.966 for active hospitalizations, R^2=0.987 for cumulative fatalities, and R^2=0.999 for cumulative vaccinations. Well-posedness, the basic reproduction number in closed form, and local and global stability of the disease-free equilibrium are established analytically. An L-infinity approximation error bound shows that the PCHIP control-node parameterization converges to the true time-varying parameters at rate O(h^2) as the node spacing vanishes. Local identifiability and a noise stability bound are established via the Fisher information matrix. A sufficient threshold condition proves epidemic decay under time-varying suppression whenever the effective reproduction number remains persistently below one. Sensitivity analyses consistently rank hospital throughput parameters above the transmission rate, providing a mathematical basis for the observation that reactive containment measures cannot prevent a hospitalization peak already driven by the pre-existing latent viral load.

math.OC↗

arXiv2014

Enhancement of the lifetime of metastable states in Er-doped Si nanocrystals by external coloured noise

The changes in the lifetime of a metastable energy level in Er-doped Si nanocrystals in the presence of an external source of colored noise are analyzed for different values of noise intensity and correlation time. Exciton dynamics is simulated by a set of phenomenological rate equations which take into account all the possible phenomena inherent to the energy states of Si nanocrystals and Er$^{3+}$ ions in the host material of Si oxide. The electronic deexcitation is studied by examining the decay of the initial population of the Er atoms in the first excitation level $^4$I$_{13/2}$ through the fluorescence and the cooperative upconversion by energy transfer. Our results show that the deexcitation process of the level $^4$I$_{13/2}$ is slowed down within wide ranges of noise intensity and correlation time. Moreover, a nonmonotonic behavior of the lifetime with the amplitude of the fluctuations is found, characterized by a maximum variation for values of the noise correlation time comparable to the deexcitation time. The indirect influence of the colored noise on the efficiency of the energy transfer upconversion activated from the level $^4$I$_{13/2}$ is also discussed.

cond-mat.mtrl-sci↗

arXiv2013

Enhancement of electron spin lifetime in GaAs crystals: the benefits of dichotomous noise

The electron spin relaxation process in n-type GaAs crystals driven by a fluctuating electric field is investigated. Two different sources of fluctuations are considered: (i) a symmetric dichotomous noise and (ii) a Gaussian correlated noise. Monte Carlo numerical simulations show, in both cases, an enhancement of the spin relaxation time by increasing the amplitude of the external noise. Moreover, we find that the electron spin lifetime versus the noise correlation time: (i) increases up to a plateau in the case of dichotomous random fluctuations, and (ii) shows a nonmonotonic behaviour with a maximum in the case of bulks subjected to a Gaussian correlated noise.

cond-mat.mes-hall↗

arXiv2012

Doping dependence of spin dynamics of drifting electrons in GaAs bulks

We study the effect of the impurity density on lifetimes and relaxation lengths of electron spins in the presence of a static electric field in a n-type GaAs bulk. The transport of electrons and the spin dynamics are simulated by using a semiclassical Monte Carlo approach, which takes into account the intravalley scattering mechanisms of warm electrons in the semiconductor material. Spin relaxation is considered through the D'yakonov-Perel mechanism, which is the dominant mechanism in III-V semiconductors. The evolution of spin polarization is analyzed by computing the lifetimes and depolarization lengths as a function of the doping density in the range 10^{13} - 10^{16} cm^{-3}, for different values of the amplitude of the static electric field (0.1 - 1.0 kV/cm). We find an increase of the electron spin lifetime as a function of the doping density, more evident for lattice temperatures lower than 150 K. Moreover, at very low intensities of the driving field, the spin depolarization length shows a nonmonotonic behaviour with the density. At the room temperature, the spin lifetimes and depolarization lengths are nearly independent on the doping density. The underlying physics is analyzed.

cond-mat.other↗

arXiv2011

New insights into electron spin dynamics in the presence of correlated noise

The changes of the spin depolarization length in zinc-blende semiconductors when an external component of correlated noise is added to a static driving electric field are analyzed for different values of field strength, noise amplitude and correlation time. Electron dynamics is simulated by a Monte Carlo procedure which keeps into account all the possible scattering phenomena of the hot electrons in the medium and includes the evolution of spin polarization. Spin depolarization is studied by examinating the decay of the initial spin polarization of the conduction electrons through the D'yakonov-Perel process, the only relevant relaxation mechanism in III-V crystals. Our results show that, for electric field amplitude lower than the Gunn field, the dephasing length shortens with the increasing of the noise intensity. Moreover, a nonmonotonic behavior of spin depolarization length with the noise correlation time is found, characterized by a maximum variation for values of noise correlation time comparable with the dephasing time. Instead, in high field conditions, we find that, critically depending on the noise correlation time, external fluctuations can positively affect the relaxation length. The influence of the inclusion of the electron-electron scattering mechanism is also shown and discussed.

cond-mat.stat-mech↗

arXiv2011

Temperature dependence of spin depolarization of drifting electrons in n-type GaAs bulks

The influence of temperature and transport conditions on the electron spin relaxation in lightly doped n-type GaAs semiconductors is investigated. A Monte Carlo approach is used to simulate electron transport, including the evolution of spin polarization and relaxation, by taking into account intravalley and intervalley scattering phenomena of the hot electrons in the medium. Spin relaxation lengths and times are computed through the D'yakonov-Perel process, which is the more relevant spin relaxation mechanism in the regime of interest (10 < T < 300 K). The decay of the initial spin polarization of the conduction electrons is calculated as a function of the distance in the presence of a static electric field varying in the range 0.1 - 2 kV/cm. We find that the electron spin depolarization lengths and times have a nonmonotonic dependence on both the lattice temperature and the electric field amplitude.

cond-mat.stat-mech↗

arXiv2011

Relaxation of Electron Spin during High-Field Transport in GaAs Bulk

A semiclassical Monte Carlo approach is adopted to study the multivalley spin depolarization of drifting electrons in a doped n-type GaAs bulk semiconductor, in a wide range of lattice temperature ($40<T_L<300$ K) and doping density ($10^{13}<n<10^{16}$cm$^{-3}$). The decay of the initial non-equilibrium spin polarization of the conduction electrons is investigated as a function of the amplitude of the driving static electric field, ranging between 0.1 and 6 kV/cm, by considering the spin dynamics of electrons in both the $Γ$ and the upper valleys of the semiconductor. Doping density considerably affects spin relaxation at low temperature and weak intensity of the driving electric field. At high values of the electric field, the strong spin-orbit coupling of electrons in the $L$-valleys significantly reduces the average spin polarization lifetime, but, unexpectedly, for field amplitudes greater than 2.5 kV/cm, the spin lifetime increases with the lattice temperature. Our numerical findings are validated by a good agreement with the available experimental results and with calculations recently obtained by a different theoretical approach.

cond-mat.stat-mech↗

arXiv2008

Evolutionary dynamics of imatinib-treated leukemic cells by stochastic approach

The evolutionary dynamics of a system of cancerous cells in a model of chronic myeloid leukemia (CML) is investigated by a statistical approach. Cancer progression is explored by applying a Monte Carlo method to simulate the stochastic behavior of cell reproduction and death in a population of blood cells which can experience genetic mutations. In CML front line therapy is represented by the tyrosine kinase inhibitor imatinib which strongly affects the reproduction of leukemic cells only. In this work, we analyze the effects of a targeted therapy on the evolutionary dynamics of normal, first-mutant and cancerous cell populations. Several scenarios of the evolutionary dynamics of imatinib-treated leukemic cells are described as a consequence of the efficacy of the different modeled therapies. We show how the patient response to the therapy changes when an high value of the mutation rate from healthy to cancerous cells is present. Our results are in agreement with clinical observations. Unfortunately, development of resistance to imatinib is observed in a proportion of patients, whose blood cells are characterized by an increasing number of genetic alterations. We find that the occurrence of resistance to the therapy can be related to a progressive increase of deleterious mutations.

q-bio.PE↗