COVID-19 and other viruses: Holding back its spreading by massive testing.

The experience of Singapore and South Korea makes it clear that under certain circumstances massive testing is an effective way for containing the advance of the COVID-19. In this paper, we propose a modified SEIR model which takes into account tracing and massive testing, proving theoretically that more tracing and testing implies a reduction of the total number of infected people in the long run. We apply this model to the spreading of the first wave of the disease in Spain, obtaining numerical results. After that, we introduce a heuristic approach in order to minimize the COVID-19 spreading by planning effective test distributions among the populations of a region over a period of time. As an application, the impact of distributing tests among the counties of New York according to this method is computed in terms of the number of saved infected individuals. • A SEIR model is introduced to analyse the efficiency of test distributions. • It is proved that massive testing helps reducing the number of infected people. • A heuristic method is developed to increase the number of saved infections. • Extensive computational experience quantifies the number of saved infections. [ABSTRACT FROM AUTHOR]