Unsupervised learning for economic risk evaluation in the context of Covid-19 pandemic

TL;DR

This paper presents an unsupervised machine learning system that predicts economic impact related to COVID-19 case surges, aiding policy decisions while emphasizing low maintenance and computational efficiency, deployed via a web app in Colombia.

Contribution

The work introduces a novel unsupervised learning approach integrating time series forecasting to assess economic impact during COVID-19, with a focus on practical deployment and low resource requirements.

Findings

System accurately predicts economic impact based on COVID-19 case forecasts.

Deployed as a web application for real-time data analysis in Colombia.

Designed for low maintenance and computational efficiency.

Abstract

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).