A Study of Data-driven Methods for Adaptive Forecasting of COVID-19 Cases

TL;DR

This paper investigates data-driven methods for adaptive COVID-19 case forecasting, emphasizing incremental learning to handle data scarcity and virus transmissibility variability, with extensive empirical evaluation across different outbreak phases.

Contribution

It introduces an incremental learning framework for COVID-19 case prediction and provides comprehensive empirical analysis of its performance during different virus waves.

Findings

Incremental learning improves prediction accuracy during outbreaks.

Feature extraction and window size significantly affect model performance.

The framework adapts well to nonstationary data conditions.

Abstract

Severe acute respiratory disease SARS-CoV-2 has had a found impact on public health systems and healthcare emergency response especially with respect to making decisions on the most effective measures to be taken at any given time. As demonstrated throughout the last three years with COVID-19, the prediction of the number of positive cases can be an effective way to facilitate decision-making. However, the limited availability of data and the highly dynamic and uncertain nature of the virus transmissibility makes this task very challenging. Aiming at investigating these challenges and in order to address this problem, this work studies data-driven (learning, statistical) methods for incrementally training models to adapt to these nonstationary conditions. An extensive empirical study is conducted to examine various characteristics, such as, performance analysis on a per virus wave basis, feature extraction, "lookback" window size, memory size, all for next-, 7-, and 14-day forecasting tasks. We demonstrate that the incremental learning framework can successfully address the aforementioned challenges and perform well during outbreaks, providing accurate predictions.