In October 2018, Andrea Gatto obtained the bachelor’s degree, cum laude, in Information Engineering at the University of Salento with a thesis in Principles of software design entitled “Android porting of a stand-alone java application for the end-users of a sports facility”. In October 2020, he obtained his master’s degree, cum laude, in Computer Engineering at the University of Salento with a thesis in High Performance Computing entitled “Forecasting the COVID-19 spread in Lombardy using SEIR compartmental models”.
His research interests include theoretical, experimental, and methodological aspects related to Machine Learning, Deep Learning, Web and Mobile Agile Modeling and Development. Furthermore, he has been involved in a depth study of agent-based compartmental modelling techniques like SIR, SEIR and MSEIRS models.
His latest activities involve Machine Learning and novel hybrid frameworks to make forecasts regarding COVID-19 spread in Italy.
- An Artificial Neural Network-based approach for predicting the COVID-19 daily effective reproduction number Rt in Italy
- MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain
- The effect of known and unknown confounders on the relationship between air pollution and Covid-19 mortality in Italy: A sensitivity analysis of an ecological study based on the E-value
- Limits of Compartmental Models and New Opportunities for Machine Learning: A Case Study to Forecast the Second Wave of COVID-19 Hospitalizations in Lombardy, Italy