  Currently there are many attempts around the world to use computers,
smartphones, tablets and other electronic devices in order to stop the spread
of COVID-19. Most of these attempts focus on collecting information about
infected people, in order to help healthy people avoid contact with them.
However, social distancing decisions are still taken by the governments
empirically. That is, the authorities do not have an automated tool to
recommend which decisions to make in order to maximize social distancing and to
minimize the impact for the economy. In this paper we address the
aforementioned problem and we design an algorithm that provides social
distancing methods (i.e., what schools, shops, factories, etc. to close) that
are efficient (i.e., that help reduce the spread of the virus) and have low
impact on the economy. On short: a) we propose several models (i.e.,
combinatorial optimization problems); b) we show some theoretical results
regarding the computational complexity of the formulated problems; c) we give
an algorithm for the most complex of the previously formulated problems; d) we
implement and test our algorithm; and e) we show an integer linear program
formulation for our problem.
