A5041668511- Regional Socio-Economic Development Trends
- COVID-19, Geopolitics, Technology, Migration
- Impact of AI and Big Data on Business and Society
- COVID-19 epidemiological studies
- Anomaly Detection Techniques and Applications
- SARS-CoV-2 and COVID-19 Research
- Influenza Virus Research Studies
- COVID-19 Clinical Research Studies
- Control Systems and Identification
- Energy Load and Power Forecasting
- Calcium Carbonate Crystallization and Inhibition
- Fault Detection and Control Systems
- Data-Driven Disease Surveillance
- Bone Tissue Engineering Materials
- COVID-19 Pandemic Impacts
- Radiomics and Machine Learning in Medical Imaging
- Model Reduction and Neural Networks
- Advanced X-ray and CT Imaging
- Cellular and Composite Structures
- Bone health and osteoporosis research
University of Milan
2025
Politecnico di Milano
2021-2023
Johns Hopkins University
2023
Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields recent insights epidemiology, one maximise the predictive performance such if multiple models are combined into an ensemble. Here, we report ensembles predicting COVID-19 cases deaths across Europe between 08 March 2021 07 2022.
We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of COVID-19 epidemic, which we validate using data from Italy starting in September 2020. SEIHRDV features following compartments: Susceptible (S), Exposed (E), Infectious (I), Healing (H), Recovered (R), Deceased (D) and Vaccinated (V). The model is age-stratified, it considers population split into 15 age groups. Moreover, takes account 7 different contexts exposition infection (family, home,...
We propose a novel epidemiological model, referred to as SEIHRDV, for the numerical simulation of COVID-19 epidemic, validated using data from Italy starting in September 2020. SEIHRDV includes following compartments: Susceptible (S), Exposed (E), Infectious (I), Healing (H), Recovered (R), Deceased (D), and Vaccinated (V). The model is age-stratified, with population divided into 15 age groups, it considers seven different contexts exposure infection (family, home, school, work, transport,...
In the context of SARS-CoV-2 pandemic, mathematical modelling has played a fundamental role for making forecasts, simulating scenarios and evaluating impact preventive political, social pharmaceutical measures. Optimal control theory represents useful tool to plan vaccination campaign aimed at eradicating pandemic as fast possible. The aim this work is explore optimal prioritisation order planning campaigns able achieve specific goals, reduction amount infected, deceased hospitalized in...
Abstract Background Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields recent insights epidemiology, one maximise the predictive performance such if multiple models are combined into an ensemble. Here we report ensembles predicting COVID-19 cases deaths across Europe between 08 March 2021 07 2022. Methods We used open-source tools develop a public European Forecast Hub. invited groups...
The solutions provided through natural evolution of living creatures serve as an ingenious source inspiration for many technological and applicative fields. Along these lines, bone-inspired concepts lead to fascinating advances in product design, architecture garments, thanks the bone’s exceptional combination strength, toughness lightness. Structural applications are inspired by ability resist fracture under a large spectrum forces, while high surface area pore connectivity bone present...
Since infectious pathogens start spreading into a susceptible population, mathematical models can provide policy makers with reliable forecasts and scenario analyses, which be concretely implemented or solely consulted. In these complex epidemiological scenarios, machine learning architectures play an important role, since they directly reconstruct data-driven circumventing the specific modelling choices parameter calibration, typical of classical compartmental models. this work, we discuss...
In this work, we aim to formalize a novel scientific machine learning framework reconstruct the hidden dynamics of transmission rate, whose inaccurate extrapolation can significantly impair quality epidemic forecasts, by incorporating influence exogenous variables (such as environmental conditions and strain-specific characteristics). We propose an hybrid model that blends data-driven layer with physics-based one. The is based on neural ordinary differential equation learns conditioned...