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The identification of the transmission parameters of a virus is fundamental to identify the optimal public health strategy. These parameters can present significant changes over time caused by genetic mutations or viral recombination, making their continuous monitoring fundamental. Here we present a method, suitable for this task, which uses as unique information the daily number of reported cases. The method is based on a time since infection model where transmission parameters are obtained by means of an efficient maximization procedure of the likelihood. Applying the method to SARS-CoV-2 data in Italy, we find an average generation time z ¯ = 3.2 ± 0.8 days, during the temporal window when the majority of infections can be attributed to the Omicron variants. At the same time we find a significantly larger value z ¯ = 6.2 ± 1.1 days, in the temporal window when spreading was dominated by the Delta variant. We are also able to show that the presence of the Omicron variant, characterized by a shorter z ¯ , was already detectable in the first weeks of December 2021, in full agreement with results provided by sequences of SARS-CoV-2 genomes reported in national databases. Our results therefore show that the novel approach can indicate the existence of virus variants, resulting particularly useful in situations when information about genomic sequencing is not yet available. At the same time, we find that the standard deviation of the generation time does not significantly change among variants. [ABSTRACT FROM AUTHOR]

Switched capacitor converters with ideal switches exhibit discontinuities in the electrical variables, i.e., state jumps at the switching time instants. This feature introduces some difficulties for the determination of compact dynamic models for these power converters. In this paper, we tackle the issue by considering a switched model where the dynamics of the modes are represented through differential algebraic equations. For this class of systems the classical averaged model fails. By including a suitable jump mode that approximates the state discontinuities, a generalized frequency-dependent averaged model is proposed. The effectiveness of the switched and averaged models is numerically and experimentally verified by analyzing the ladder, series–parallel, Fibonacci, and Dickson topologies, and by considering different switching frequencies, voltage sources, and circuits parameters. [ABSTRACT FROM PUBLISHER]

This article proposes a novel kernel-based Dynamic Programming (DP) approximation method to tackle the typical curse of dimensionality of stochastic DP problems over the finite time horizon. Such a method utilizes kernel functions in combination with Support Vector Machine (SVM) regression to determine an approximate cost function for the entire state space of the underlying Markov Decision Process (MDP), by leveraging cost function computed for selected representative states. Kernel functions are used to define the so-called kernel matrix, while the parameter vector of the given kernel-based cost function approximation is computed by moving backwards in time from the terminal condition and by applying SVM regression. This way, the difficulty of selecting a proper set of features is also tackled. The proposed method is then extended to the infinite time horizon case. To show the effectiveness of the proposed approach, the resulting Recursive Residual Approximate Dynamic Programming (RR-ADP) algorithm is applied to the sensor scheduling design in multi-process remote state estimation problems. [ABSTRACT FROM AUTHOR]