Group Testing with a Graph Infection Spread Model
نویسندگان
چکیده
The group testing idea is an efficient infection identification approach based on pooling the test samples of a individuals, which results in with less number tests than individually population. In our work, we propose novel spread model random connection graph represents connections between n individuals. Infection spreads via and this probabilistic cluster formation structure as well non-i.i.d. (correlated) statuses for We class two-step sampled algorithms where exploit known model. investigate metrics associated algorithms. To demonstrate results, analytically tractable exponentially split trees, calculate required expected false classifications terms system parameters, identify trade-off them. For such zero-error construction, prove that O(log2n). Thus, algorithm outperforms any non-adaptive test, binary splitting algorithm, Hwang’s generalized algorithm. Our imply that, by exploiting information can be used to reduce significantly even when rate high, contrasting prevalent belief useful only low.
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ژورنال
عنوان ژورنال: Information
سال: 2023
ISSN: ['2078-2489']
DOI: https://doi.org/10.3390/info14010048