Learning from Neighbors with PHIBP: Predicting Infectious Disease Dynamics in Data-Sparse Environments
Published:Dec 25, 2025 05:00
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This ArXiv paper introduces the Poisson Hierarchical Indian Buffet Process (PHIBP) as a solution for predicting infectious disease outbreaks in data-sparse environments, particularly regions with historically zero cases. The PHIBP leverages the concept of absolute abundance to borrow statistical strength from related regions, overcoming the limitations of relative-rate methods when dealing with zero counts. The paper emphasizes algorithmic implementation and experimental results, demonstrating the framework's ability to generate coherent predictive distributions and provide meaningful epidemiological insights. The approach offers a robust foundation for outbreak prediction and the effective use of comparative measures like alpha and beta diversity in challenging data scenarios. The research highlights the potential of PHIBP in improving infectious disease modeling and prediction in areas where data is limited.
Key Takeaways
- •PHIBP addresses the challenge of predicting infectious disease outbreaks in data-sparse environments.
- •The model borrows statistical strength from related regions using the concept of absolute abundance.
- •Experimental results demonstrate the framework's ability to generate coherent predictive distributions and provide epidemiological insights.
Reference
“The PHIBP's architecture, grounded in the concept of absolute abundance, systematically borrows statistical strength from related regions and circumvents the known sensitivities of relative-rate methods to zero counts.”