A data-driven epidemic model with social structure for understanding the COVID-19 infection on a heavily affected Italian province

DOI10.1142/S021820252150055XzbMath1481.92174arXiv2103.06027MaRDI QIDQ5024413

Silvia Deandrea, Giacomo Dimarco, M. Azzi, Pietro Perotti, Silvia Figini, Mattia Zanella, Chiara Bardelli, Giuseppe Toscani

Publication date: 31 January 2022

Published in: Mathematical Models and Methods in Applied Sciences (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.06027

zbMATH Keywords

data analysisdisease controluncertainty quantificationepidemic modelsnonlinear incidence ratesocial contactsdata driven modelinghealthcare systemvaccination campaign

Mathematics Subject Classification ID

Epidemiology (92D30) PDEs in connection with biology, chemistry and other natural sciences (35Q92) Models of societies, social and urban evolution (91D10) Medical epidemiology (92C60)

Related Items (8)

Kinetic Modelling of Epidemic Dynamics: Social Contacts, Control with Uncertain Data, and Multiscale Spatial Dynamics ⋮ On the optimal control of kinetic epidemic models with uncertain social features ⋮ Human behavioral crowds review, critical analysis and research perspectives ⋮ Advanced models for COVID-19 variant dynamics and pandemic waves ⋮ Kinetic models for epidemic dynamics in the presence of opinion polarization ⋮ From agent-based models to the macroscopic description of fake-news spread: the role of competence in data-driven applications ⋮ Modeling nonlocal behavior in epidemics via a reaction-diffusion system incorporating population movement along a network ⋮ Modeling virus pandemics in a globally connected world a challenge towards a mathematics for living systems

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