Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread
This work addresses the problem of accurately predicting COVID-19 dynamics for public health planning, though it appears incremental as it combines existing unsupervised and supervised methods in a semi-supervised framework.
The authors tackled modeling COVID-19 spread by developing a semi-supervised neural network that solves an inverse problem to estimate optimal conditions from real data, resulting in predictions for infection, recovery, and death dynamics, as well as the basic reproduction number for various countries.
Studying the dynamics of COVID-19 is of paramount importance to understanding the efficiency of restrictive measures and develop strategies to defend against upcoming contagion waves. In this work, we study the spread of COVID-19 using a semi-supervised neural network and assuming a passive part of the population remains isolated from the virus dynamics. We start with an unsupervised neural network that learns solutions of differential equations for different modeling parameters and initial conditions. A supervised method then solves the inverse problem by estimating the optimal conditions that generate functions to fit the data for those infected by, recovered from, and deceased due to COVID-19. This semi-supervised approach incorporates real data to determine the evolution of the spread, the passive population, and the basic reproduction number for different countries.