Press article, video or other popular media
Year
2025
Abstract
The accurate prediction of infectious disease spread is a fundamental challenge in epidemiology. Effective public health interventions rely on precise forecasts to anticipate outbreaks, allocate resources, and implement timely containment measures. However, epidemic data is often incomplete or unreliable due to underreporting, limited testing, and privacy concerns. Many infections remain undetected, particularly in the early stages of an outbreak, and the structure of interactions within a population adds further complexity to modeling disease transmission. These limitations introduce significant uncertainty into traditional epidemiological models, which depend on high-quality data to make accurate predictions.
et KLOPP, O. (2025). Denoising Epidemic Data: A New Approach to Improving Disease Spread Predictions. ESSEC Knowledge.