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.
Highlights
The unexpected apparition of Corona Virus Disease (Covid-19 in short) [1] has reconfigured the current policies of public health of global operators, forcing them to apply the more robust schemes of recovering and surveillance in the shortest times without an optimal usage of resources: Times, materials anf human resources
This paper tries to answer the question: To what extent the schemes of machine learning seen as an universal computational tool can be useful to understand recent data of data from infections by Covid-19?
This paper has assumed to priori that the time evolution of rate of infections is to some extent dictated by the rules that govern the propagation as commonly seen in physics and that was developed by Feynman [2]
Summary
The unexpected apparition of Corona Virus Disease (Covid-19 in short) [1] has reconfigured the current policies of public health of global operators, forcing them to apply the more robust schemes of recovering and surveillance in the shortest times without an optimal usage of resources: Times, materials anf human resources. Current data between March and July exhibit peaks and fluctuations, facts that would reinforce the hypothesis that in more cases (countries) the dynamics of spread and subsequent infections by Covid-19 appears to be strongly related to randomness In this manner, this paper has assumed to priori that the time evolution of rate of infections is to some extent dictated by the rules that govern the propagation as commonly seen in physics and that was developed by Feynman [2]. Once the problem of spread and infection is modeled through the propagator theory, this work has opted by the philosophy of Machine Learning in order to translate the language of dataset in terms of the view of Tom Mitchell [3] that states that all system can be universally described by actions, (i) task, (ii) performance, and (iii) experience In this manner one can use this methodology to extract information from any statistical dataset, such as the ones recently have been taken due to the Covid-19 pandemic.
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