Abstract: In this paper, the criteria of Tom Mitchell based at the philosophy of Machine Learning have been used to interpret data of new cases per week of infections by Covid-19 at Per´u. For this, it was constructed a mathematical scheme that encloses the Mitchell’s criteria as well as the idea of propagation as commonly used in modern physics to attack complex problems of interactions. With this, both the 2009 season of AH1N1 flu outbreak and the ongoing Covid-19 data were analyzed in terms of task, performance and experience. In contrast with the AH1N1 case, the Covid-19 data do not exhibit any performance in terms of minimize infections at the first weeks of the beginning of the outbreak, suggesting that precise actions to reduce infections have not been taken appropriately.

Huber Nieto-Chaupis, “Modeling and Interpretation of Covid-19 Infections Data at Peru through the Mitchell’s Criteria” International Journal of Advanced Computer Science and Applications(IJACSA), 11(9), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110986

@article{Nieto-Chaupis2020,
title = {Modeling and Interpretation of Covid-19 Infections Data at Peru through the Mitchell’s Criteria},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110986},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110986},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {9},
author = {Huber Nieto-Chaupis}
}