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).

翻译：在Covid-19大流行期间采取严厉措施是困难的,这不仅是因为限制个人权利,而且因为其经济影响。这项工作的目的是提出一种机械学习方法,以确定应该执行类似卫生政策的区域。为此,我们成功地开发了一个系统,根据通过未经监督的学习和时间序列预测的新的附带案例的预测,给出了经济影响的概念。该系统的建立考虑到了计算限制和低维护要求,以提高系统的复原力。最后,该系统作为哥伦比亚COVID-19模拟和数据分析的网络应用软件的一部分,在哥伦比亚(https://covid19.dis.eafit.edu.co)提供。