Publications by authors named "Mario Zanon"
Article Synopsis
- Vaccination campaigns against SARS-CoV-2 are challenged by ensuring fair and effective distribution of limited vaccine doses, currently based on criteria such as age and risk.
- Researchers propose a new approach focusing on spatial allocation strategies, which consider the geographical variations in disease transmission and history to enhance vaccine distribution.
- Using a novel optimal control framework, they analyzed Italy's COVID-19 spread to find that their optimized allocation method significantly outperforms traditional strategies based on incidence or population, highlighting the need for nuanced and location-specific vaccination plans.
Exponential Random Graph Models (ERGMs) have gained increasing popularity over the years. Rooted into statistical physics, the ERGMs framework has been successfully employed for reconstructing networks, detecting statistically significant patterns in graphs, counting networked configurations with given properties. From a technical point of view, the ERGMs workflow is defined by two subsequent optimization steps: the first one concerns the maximization of Shannon entropy and leads to identify the functional form of the ensemble probability distribution that is maximally non-committal with respect to the missing information; the second one concerns the maximization of the likelihood function induced by this probability distribution and leads to its numerical determination.
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