Advances Brain Research and Neuroscience

Efficient Detection of Rare Disease Hotspots Using Fuzzy Adaptive Cluster Sampling under Inhomogeneous Spatial Point Processes

Abstract

Dr. Janardan Behera

Reliable detection and estimation of rare disease hotspots are fundamentally hindered by low prevalence, weak spatial signals, and severe operational constraints in surveillance systems. Classical adaptive cluster sampling, although theoretically suitable for rare events, often suffers from sharp instability due to rigid threshold-based triggering and uncontrolled cluster expansion. This study develops a novel fuzzy adaptive cluster sampling framework for hotspot detection and inference under inhomogeneous spatial point processes by replacing binary triggering with probabilistic, membership- driven adaptive expansion. A complete design-based inferential structure is established through de- fuzzified Horvitz–Thompson type estimators, Monte Carlo approximation of inclusion probabilities, and network-level variance estimation, with formal proofs of design unbiasedness, consistency, and variance stabilization. Extensive simulation experiments based on inhomogeneous Poisson spatial point process models with embedded rare disease hotspots demonstrate that the proposed fuzzy adaptive design consistently achieves higher detection sensitivity under weak and diffuse clustering while maintaining controlled false alarm rates and stable sampling effort. In-depth sensitivity tests looking at fuzziness levels, contrast between hotspots and backgrounds, spatial neighborhood patterns, and how rare the disease is further back up how strong and dependable this system is in practice. The results establish fuzzy adaptive cluster sampling as a statistically principled, op- erationally stable, and practically implementable methodology for modern rare disease surveillance under spatial uncertainty.

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