论文标题
无监督的学习在COVID-19大流行中进行经济风险评估
Unsupervised learning for economic risk evaluation in the context of Covid-19 pandemic
论文作者
论文摘要
在199年大流行期间为严厉的措施辩护，这不仅是由于对个人权利的限制，而且还因为其经济影响。这项工作的目的是提出一种机器学习方法，以识别应实施类似健康政策的地区。为此，我们成功地开发了一个系统，该系统通过无监督的学习和时间序列预测预测了新的偶然案例，从而产生了经济影响的概念。考虑到计算限制和低维护要求，以提高系统的弹性。最后，该系统是作为Web应用程序的一部分，用于哥伦比亚的Covid-19的模拟和数据分析，网址为（https://covid19.dis.eafit.edu.co）。
Justifying draconian measures during the Covid-19 pandemic was difficult not only because of the restriction of individual rights, but also because of its economic impact. The objective of this work is to present a machine learning approach to identify regions that should implement similar health policies. For that end, we successfully developed a system that gives a notion of economic impact given the prediction of new incidental cases through unsupervised learning and time series forecasting. This system was built taking into account computational restrictions and low maintenance requirements in order to improve the system's resilience. Finally this system was deployed as part of a web application for simulation and data analysis of COVID-19, in Colombia, available at (https://covid19.dis.eafit.edu.co).
