arXiv:2605.30662v1 Announce Type: new Abstract: Spatio-temporal graph-based models have typically been used to forecast new cases of infectious diseases such as COVID-19 and chickenpox outbreaks. However, the use of stochastic modelling into their learning process has been surprisingly under-investigated and rarely considered entire data sets of large countries. As a result, it is unknown whether these models would provide accurate forecasts in real-world disease spread scenarios. In this work, we propose a spatio-temporal stochastic graph-based architecture that integrates a stochastic formul
The continuous availability of real-world epidemiological data and advancements in graph-based machine learning are enabling more sophisticated disease forecasting models.
Improved infectious disease forecasting provides critical lead time for public health interventions, resource allocation, and economic planning during outbreaks.
The integration of stochastic modeling into spatio-temporal graph-based learning promises more accurate and robust predictions for real-world disease spread scenarios.
- · Public Health Agencies
- · Healthcare Providers
- · Pharmaceutical Industry
- · AI/ML Researchers
- · Ineffective Disease Tracking Systems
- · Manual Epidemiological Modeling
More precise and earlier detection of potential infectious disease outbreaks and their trajectories.
Better informed policy decisions for lockdowns, resource distribution, and vaccine/treatment development.
Enhanced global health security and potentially reduced economic disruption from future pandemics.
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