Clustering Proposal Support for the COVID-19 Making Decision Process in a Data Demanding Scenario

The COVID-19 disease surprised the world in the last months due to the number of infections and deaths have been increased in an exponential way. Since the pandemic was established by the World Health Organization, different strategies have been proposed for dealing diverse problems in cities that the coronavirus affected. This work presents a method to decision making support processes, specifically in environment with few data and variables to be considered. Thus, artificial neural networks architectures were employed to cluster the information available in the Bogota city, and provide a tool that allows generating additional findings in a simultaneous mode, and expressed as a visual map. The present proposal reached sensitivity measures around 75%, obtaining 100% for the best cases.