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

• Published in: EAI endorsed transactions on bioengineering and bioinformatics 2021-08, Vol.1 (3), p.169582
• Main Authors: Šegota, S., Lorencin, I., Anđelić, N., Štifanić, D., Musulin, J., Vlahinić, S., Šušteršič, T., Blagojević, A., Car, Z.
• Format: Article
• Language: English
• Subjects: Automation; Coronaviruses; COVID-19; Data acquisition; Epidemiology; Machine learning; Multilayer perceptrons; Training
• ISSN: 2709-4111; 2709-4111
• DOI: 10.4108/eai.4-5-2021.169582

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 e valuated 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.