  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.
