COVID-19 Predictions – A Sparse Ensemble Method

Our NeEDS Team at IMUS, Universidad de Sevilla, continues to do great work in regards to predicting the Evolution of COVID-19.  As Project Coordinator, Dolores Romero Morales, has recently stated in an interview with CBS WIRE:

“Our contribution to the collaboration is that we’re trying to build an accurate predictor for the different metrics related to Covid-19.”

“This means we’re building an algorithmic calculator that we can feed with the number of people who are in intensive care, how many hospital beds are occupied as well as how many people are carrying the virus at any given time.”

“And based on all these types of data, we’re developing a predictor using state-of-the-art machine learning methodology.”

Our NeEDS researchers – Remedios Sillero Denamiel, Cristina Molero del Río and Sandra Benítez-Peña – elaborate further on their approach to Short-Term Predictions of the Evolution of COVID-19 in this presentation: