Estimating the state of epidemics spreading with graph neural networks

• Published in: Nonlinear dynamics Vol. 109; no. 1; pp. 249 - 263
• Main Authors: Tomy, Abhishek, Razzanelli, Matteo, Di Lauro, Francesco, Rus, Daniela, Della Santina, Cosimo
• Format: Journal Article
• Language: English
• Published: Netherlands Springer Nature B.V 01.07.2022; Springer Netherlands
• Subjects: Algorithms; Artificial neural networks; Coronaviruses; COVID-19; Deep learning; Disease transmission; Epidemics; Graph neural networks; Kalman filters; Metropolitan areas; Neural networks; Original Paper; Pandemics; Population; Robotics; Social networks; CoVid-19; Epidemics; Network dynamics; Nonlinear inference; State estimation

When an epidemic spreads into a population, it is often impractical or impossible to continuously monitor all subjects involved. As an alternative, we propose using algorithmic solutions that can infer the state of the whole population from a limited number of measures. We analyze the capability of...