Machine Learning and Covid-19 Data Predict Next Intercontinental Pandemic

Abstract

Experiences from past Covid-19 pandemic have led to explore the actions that were taken previous time to the implementation of policies in a fast and optimal manner. Because of this actions the arrival of virus to a country would have to have exhibited a reduced number of infections and fatalities. Nevertheless it was not in that way as was observed in the global data, with a pandemic showing peaks of infections, waves and various virus mutations. This is the central focus of this paper: To understand the global data, so that one can employ this knowledge to identify as well as anticipate the possible apparition of a new virus. In this manner, this paper combines that Covid-19 global data and the criteria of Tom Mitchell to identify the levels of lethality of a new virus. To accomplish this, a cognitive algorithm is developed and it has as central purpose to find the matching between previous pandemic and new data of a pandemic in its first phase. As illustration, up to 6 countries were examined to assess their strengths again a new virus.