Automated Pipeline for Continual Data Gathering and Retraining of the Machine Learning-Based COVID-19 Spread Models

Abstract

INTRODUCTION: The development of epidemiological curve models is one of the key factors in the combat of epidemiological diseases such as COVID-19. OBJECTIVES: The goal of this paper is to develop a system for automatic training and testing of AI-based regressive models of epidemiological curves using public data,which involves automating the data acquisition and speeding up the training of the models. METHODS: The research applies Multilayer Perceptron (MLP) for the creation of models,implemented within a system for automatic data fetching and training,and evaluated using the coefficient of determination (R2). Training time is lowered through the application of data filtering and simplifying the model selection. RESULTS: The developed system can train high precision models rapidly,allowing for quick model delivery All trained models achieve scores which are higher than 0.95. CONCLUSION: The results show that the development of a quick COVID-19 spread modeling system is possible.