Special Issue: Collective models, control and uncertainty quantification in socio-epidemic dynamics
Guest Editors
Prof. Lorenzo Pareschi
Department of Mathematics and Computer Science, University of Ferrara, Via Machiavelli 30, I-44121 Ferrara, Italy
Email: lorenzo.pareschi@unife.it
Prof. Giuseppe Toscani
Department of Mathematics, University of Pavia, Via Ferrata 1, I-27100 Pavia, Italy
Email: giuseppe.toscani@unipv.it
Prof. Andrea Tosin
Department of Mathematical Sciences “G. L. Lagrange", Politecnico di Torino, Corso Duca degli Abruzzi 24, I-10129 Torino, Italy
Email: andrea.tosin@polito.it
Manuscript Topics
This special issue stems from the desire to take a step towards a greater understanding of the complex historical moment we are going through. The current health emergency due to the pandemic has triggered profound changes in modern society. The mathematical study of the spread of epidemics is a well-established scientific field dating back to the early 20th century. On the contrary, a systematic mathematical framework for studying the relationships between the epidemic and its socio-economic impact is still lacking. In this perspective, this special issue aims at gathering a collection of contributions that go beyond the classic paradigm of compartmental models in epidemiology and are inspired by some recent advances in the modeling of collective dynamics in social sciences through kinetic and mean-field models.
The special issue foresees the submission of articles in mathematical and statistical modeling in epidemiology, with the integration of those modeling and computational aspects proper to the research of collective behavior in the socio-economic field. The mathematical topics of interest in this context range from kinetic modeling and related differential problems to control problems and more strictly computational aspects such as uncertainty quantification.
A list of specific research topics is reported below:
• Collective models and their applications in sociology and epidemiology (contact dynamics, socially structured models, wealth distributions…)
• Mathematical modeling and analysis of the spatial spread of epidemic diseases (transport models, diffusion models, models on networks,…)
• Dealing with uncertain data in socio-epidemic phenomena (stochastic models, uncertainty quantification, numerical approaches,…)
• Implementation of suitable control strategies (confinement and lockdown policies, social distancing, optimal vaccination plans, …)
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