Moving From COVID-19 Mathematical Models to Vaccine Design: Theory, Practice and Experiences

Data Science: A Useful Tool for Understanding SARS-CoV-2 Information Facts

Author(s): Nicandro Cruz-Ramírez, Guillermo-de-Jesús Hoyos-Rivera*, Sonia-Lilia Mestizo-Gutiérrez and Horacio Tapia-McClung

Pp: 238-275 (38)

DOI: 10.2174/9789815051902122010011

* (Excluding Mailing and Handling)


By the end of 2019, a local pneumonia outbreak in Wuhan, China, was determined to be caused by a novel form of coronavirus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which, since then, has spread worldwide thus becoming the most important public health issue of the beginning of the 21st century. As a response, governments and health organizations started collecting data about this disease for analyzing it, trying to draw conclusions that could lead to a better understanding of it, and eventually to alleviate its effects. Through the analysis of the available data, our area of interest has to do with finding possible correlations among the variables related to COVID-19 cases which could give some insights. For example, which factors make a given patient to present an aggravated form of this illness, or even a higher risk of dying? Does the level of poverty make some people prone to get ill? What about the place where people live in? These and other questions can be answered based on the analysis of the available data. In this chapter, we give a brief introduction to Data Science (DS), a field of Artificial Intelligence (AI), and present some data analysis using publicly available COVID-19 data sets provided by the Mexican government. This allows us to show how AI tools and techniques can help us to better understand some aspects of this kind of situation, and in this way, hopefully helping health officials and providers to create better health policies and services and succeeding in their goal: saving lives.

Keywords: Artificial Intelligence, Bayesian Network, COVID-19, Database, Data Science, Decision Tree, Entropy, Epidemiology, Health, Information, Knowledge, Pandemic, SARS-CoV-2, Workflow

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