Learning from Neighbors with PHIBP: Predicting Infectious Disease Dynamics in Data-Sparse Environments

• URL: http://arxiv.org/abs/2512.21005v1
• Date: Wed, 24 Dec 2025 07:10:17 GMT
• Title: Learning from Neighbors with PHIBP: Predicting Infectious Disease Dynamics in Data-Sparse Environments
• Abstract summary: We present the detailed computational framework and experimental application of the Poisson Hierarchical Indian Buffet Process (PHIBP)<n>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.<n>We show that this principled approach provides a robust foundation for generating coherent predictive distributions and for the effective use of comparative measures such as alpha and beta diversity.
• Score: 14.991529825278477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modeling sparse count data, which arise across numerous scientific fields, presents significant statistical challenges. This chapter addresses these challenges in the context of infectious disease prediction, with a focus on predicting outbreaks in geographic regions that have historically reported zero cases. To this end, we present the detailed computational framework and experimental application of the Poisson Hierarchical Indian Buffet Process (PHIBP), with demonstrated success in handling sparse count data in microbiome and ecological studies. 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. Through a series of experiments on infectious disease data, we show that this principled approach provides a robust foundation for generating coherent predictive distributions and for the effective use of comparative measures such as alpha and beta diversity. The chapter's emphasis on algorithmic implementation and experimental results confirms that this unified framework delivers both accurate outbreak predictions and meaningful epidemiological insights in data-sparse settings.

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