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TDA Engine v1.0: A Computational Framework for Detecting Structural Voids in Spatially Censored Epidemiological Data
2026-02-03
health informatics
Title + abstract only
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BackgroundIn public health surveillance, silence--the absence of data--is often more significant than the signal. Traditional epidemiological mapping tools efficiently visualize data density but struggle to mathematically define data absence. Standard approaches conflate stochastic sparsity with systemic suppression and remain vulnerable to edge effects. MethodsWe introduce a topological framework that detects structural voids--regions of unexpected data absence within clusters. Using Distance-...
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