Motivated by the recent outbreak of coronavirus (COVID-19), we propose a stochastic model of epidemic temporal growth and mitigation based on a time-modulated Hawkes process. The model is sufficiently rich to incorporate specific characteristics of the novel coronavirus, to capture the impact of undetected, asymptomatic and super-diffusive individuals, and especially to take into account time-varying counter-measures and detection efforts. Yet, it is simple enough to allow scalable and efficient computation of the temporal evolution of the epidemic, and exploration of what-if scenarios.

This work presents a microscale approach for simulating the dielectrophoresis assembly of polarizable particles under an external electric field. The model is shown to capture interesting dynamical and topological features, such as the formation of chains of particles and their incipient aggregation into hierarchical structures. A quantitative characterization in terms of the number and size of these structures is also discussed.

Presentazione delle attività di ricerca su computational immunology e network medicine al workshop "Hot Topics in Systems" all'interno della conferenza PLACE2019

Optimal embeddings for fractional-order Orlicz{Sobolev spaces into Campanato type s- paces are offered. This is a joint work in progress with Andrea Cianchi, Lubos Pick and Lenka Slavikova.

We consider the generalization of discrete de la Vallée Poussin means on the square, obtained via tensor product by the univariate case. Pros and cons of such a kind of filtered approximation are discussed. In particular, under simple, we get near-best discrete approximation polynomials in the space of all locally continuous functions on the square with possible algebraic singularities on the boundary, equipped with the weighted uniform norm. In the four Chebychev cases, these polynomials also interpolate the function.

The present paper represents a first methodological work for the construction of a robust and accurate algorithm for the solution of an inverse problem given by the identification of the parameters of a lumped mathematical model of fetal circulation introduced by G. Pennati et al. (1997). The underlying estimation techniques here applied are two global search meth- ods, respectively a Parameter Space Investigation (PSI) and the Ensemble Kalman Filter (EnKF), with a refinement performed with a local search method, i.e. Levenberg- Marquardt method (LM).

We prove that on a smooth bounded set, the positive least energy solution of the Lane-Emden equation with sublinear power is isolated. As a corollary, we obtain that the first (Formula presented.) eigenvalue of the Dirichlet-Laplacian is not an accumulation point of the (Formula presented.) spectrum, on a smooth bounded set. Our results extend to a suitable class of Lipschitz domains, as well.

This paper proposes a toy model where, in the Einstein equations, the right-hand side is modified by the addition of a term proportional to the symmetrized partial contraction of the Ricci tensor with the energy-momentum tensor, while the left-hand side remains equal to the Einstein tensor. Bearing in mind the existence of a natural length scale given by the Planck length, dimensional analysis shows that such a term yields a correction linear in ? to the classical term that is instead just proportional to the energy-momentum tensor.

In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability.

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simula- tions and optimization of mathematical models that contain a mathematical description of interactions between systems elements at the cellular, tissue, organ, body, and population level. This approach is the foundation of systems medicine and precision medicine. Here, simulated experiments are performed with computers (in silico) first, and they are then replicated through lab experiments (in vivo or in vitro) or clinical studies.