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

arXiv: https://arxiv.org/abs/2309.09698 · HTML: https://mlsanity.com/paper/2309.09698/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Online Learning With Adaptive Rebalancing in Nonstationary Environments](https://mlsanity.com/paper/2009.11942/) (2020)
- [Nonstationary data stream classification with online active learning and siamese neural networks](https://mlsanity.com/paper/2210.01090/) (2022)

## Built on by (most cited first, 2 of 2)

- [Investigating Forecasting Models for Pandemic Infections Using Heterogeneous Data Sources: A 2-year Study with COVID-19](https://mlsanity.com/paper/2507.12966/) (2025)
- [Cross-Country Learning for National Infectious Disease Forecasting Using European Data](https://mlsanity.com/paper/2601.20771/) (2026)

## Abstract (arXiv)

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.