Automated Machine Learning for COVID-19 Forecasting

IEEE Access, vol. 10, pp. 94718–94737

Abstract

When responding to a pandemic situation, policy makers rely on forecasts of the spread. In the context of the current COVID-19 pandemic, various sophisticated epidemic and machine learning models have been used for forecasting. These models, however, rely on carefully selected architectures and detailed data that is often only available for specific regions. Automated machine learning (AutoML) addresses these challenges by allowing to automatically create forecasting pipelines in a data-driven manner, resulting in high-quality predictions. In this paper we study the role of open data along with AutoML systems in acquiring high-performance forecasting models for COVID-19. Here, we adapted the AutoML framework auto-sklearn to the time series forecasting task and introduced two variants for multi-step ahead COVID-19 forecasting which we refer to as (a) multi-output and (b) repeated single output forecasting. We studied the usefulness of anonymized open mobility data sets (place visits, and the use of different transportation modes) in addition to open mortality data. We evaluated three drift adaptation strategies to deal with concept drifts in data by (i) refitting our models on part of the data, (ii) the full data, or (iii) retraining the models completely. We compared the performance of our AutoML methods in terms of RMSE with five baselines on two testing periods (over 2020 and 2021). Our results show that combining mobility features and mortality data improves forecasting accuracy. Furthermore, we show that when faced with concept drifts, our method refitted on recent data using place visits mobility features outperforms all other approaches for 22 of the 26 countries considered in our study.

Authors 3

1. Jaco Tetteroo Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands

2. Mitra Baratchi Aachen Leiden Institute of Advanced Computer Science

   Leiden University

   Affiliation as printed

   Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Leiden, The Netherlands

3. Holger H. Hoos Aachen Chair for AI Methodology (AIM)

   RWTH Aachen University

   Affiliation as printed

   Chair for AI Methodology (AIM), RWTH Aachen University, Aachen, Germany