# Investigating Forecasting Models for Pandemic Infections Using Heterogeneous Data Sources: A 2-year Study with COVID-19

arXiv: https://arxiv.org/abs/2507.12966 · HTML: https://mlsanity.com/paper/2507.12966/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Online Learning With Adaptive Rebalancing in Nonstationary Environments](https://mlsanity.com/paper/2009.11942/) (2020)
- [Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods](https://mlsanity.com/paper/2104.15007/) (2021)
- [A Study of Data-driven Methods for Adaptive Forecasting of COVID-19 Cases](https://mlsanity.com/paper/2309.09698/) (2023)
- [Pandemic infection forecasting through compartmental model and learning-based approaches](https://mlsanity.com/paper/2401.06629/) (2024)

## Built on by (most cited first, 1 of 1)

- [Cross-Country Learning for National Infectious Disease Forecasting Using European Data](https://mlsanity.com/paper/2601.20771/) (2026)

## Abstract (arXiv)

Emerging in December 2019, the COVID-19 pandemic caused widespread health, economic, and social disruptions. Rapid global transmission overwhelmed healthcare systems, resulting in high infection rates, hospitalisations, and fatalities. To minimise the spread, governments implemented several non-pharmaceutical interventions like lockdowns and travel restrictions. While effective in controlling transmission, these measures also posed significant economic and societal challenges. Although the WHO declared COVID-19 no longer a global health emergency in May 2023, its impact persists, shaping public health strategies. The vast amount of data collected during the pandemic offers valuable insights into disease dynamics, transmission, and intervention effectiveness. Leveraging these insights can improve forecasting models, enhancing preparedness and response to future outbreaks while mitigating their social and economic impact. This paper presents a large-scale case study on COVID-19 forecasting in Cyprus, utilising a two-year dataset that integrates epidemiological data, vaccination records, policy measures, and weather conditions. We analyse infection trends, assess forecasting performance, and examine the influence of external factors on disease dynamics. The insights gained contribute to improved pandemic preparedness and response strategies.