论文标题
在统计方法前的机器学习在哥伦比亚传播SARS-COV-2
Machine learning in front of statistical methods for prediction spread SARS-CoV-2 in Colombia
论文作者
论文摘要
使用数学模型（例如易感性暴露式诱变（SEIR），Logistic回归（LR））和一种称为多项式回归方法的机器学习方法进行了对哥伦比亚疾病共证的分析研究。先前的分析已经对每日病例，感染者以及暴露于该病毒的人进行的分析，所有这些都在550天的时间表中所有人。此外，它使感染扩散的拟合详细介绍了较低的传播误差和统计偏差的最佳方法。最后，提出了四种不同的预防方案，以评估与该疾病相关的每个参数的比率。
An analytical study of the disease COVID-19 in Colombia was carried out using mathematical models such as Susceptible-Exposed-Infectious-Removed (SEIR), Logistic Regression (LR), and a machine learning method called Polynomial Regression Method. Previous analysis has been performed on the daily number of cases, deaths, infected people, and people who were exposed to the virus, all of them in a timeline of 550 days. Moreover, it has made the fitting of infection spread detailing the most efficient and optimal methods with lower propagation error and the presence of statistical biases. Finally, four different prevention scenarios were proposed to evaluate the ratio of each one of the parameters related to the disease.
