Unsupervised learning for economic risk evaluation in the context of Covid-19 pandemic
This work provides a system for policymakers in Colombia to evaluate economic risk in regions based on COVID-19 case predictions, aiding in health policy decisions.
This paper developed an unsupervised learning system to evaluate economic risk based on predicted new COVID-19 cases. The system provides a notion of economic impact given new case predictions, and was deployed as a web application for COVID-19 simulation and data analysis in Colombia.
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).