  Whenever countries are threatened by a pandemic, as is the case with the
COVID-19 virus, governments should take the right actions to safeguard public
health as well as to mitigate the negative effects on the economy. In this
regard, there are two completely different approaches governments can take: a
restrictive one, in which drastic measures such as self-isolation can seriously
damage the economy, and a more liberal one, where more relaxed restrictions may
put at risk a high percentage of the population. The optimal approach could be
somewhere in between, and, in order to make the right decisions, it is
necessary to accurately estimate the future effects of taking one or other
measures. In this paper, we use the SEIR epidemiological model (Susceptible -
Exposed - Infected - Recovered) for infectious diseases to represent the
evolution of the virus COVID-19 over time in the population. To optimize the
best sequences of actions governments can take, we propose a methodology with
two approaches, one based on Deep Q-Learning and another one based on Genetic
Algorithms. The sequences of actions (confinement, self-isolation, two-meter
distance or not taking restrictions) are evaluated according to a reward system
focused on meeting two objectives: firstly, getting few people infected so that
hospitals are not overwhelmed with critical patients, and secondly, avoiding
taking drastic measures for too long which can potentially cause serious damage
to the economy. The conducted experiments prove that our methodology is a valid
tool to discover actions governments can take to reduce the negative effects of
a pandemic in both senses. We also prove that the approach based on Deep
Q-Learning overcomes the one based on Genetic Algorithms for optimizing the
sequences of actions.
