Anti-clustering in the national SARS-CoV-2 daily infection counts.

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Date: Aug. 27, 2021
From: PeerJ(Vol. 9)
Publisher: PeerJ. Ltd.
Document Type: Article
Length: 13,961 words
Lexile Measure: 1480L

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Abstract :

The noise in daily infection counts of an epidemic should be super-Poissonian due to intrinsic epidemiological and administrative clustering. Here, we use this clustering to classify the official national SARS-CoV-2 daily infection counts and check for infection counts that are unusually anti-clustered. We adopt a one-parameter model of [PHI]i' infections per cluster, dividing any daily count n.sub.i into ni/[PHI]i' 'clusters', for 'country' i. We assume that ni/[PHI]i' on a given day j is drawn from a Poisson distribution whose mean is robustly estimated from the four neighbouring days, and calculate the inferred Poisson probability Pij' of the observation. The Pij' values should be uniformly distributed. We find the value [PHI]i that minimises the Kolmogorov-Smirnov distance from a uniform distribution. We investigate the ([PHI].sub.i , N.sub.i ) distribution, for total infection count N.sub.i . We consider consecutive count sequences above a threshold of 50 daily infections. We find that most of the daily infection count sequences are inconsistent with a Poissonian model. Most are found to be consistent with the [PHI].sub.i model. The 28-, 14- and 7-day least noisy sequences for several countries are best modelled as sub-Poissonian, suggesting a distinct epidemiological family. The 28-day least noisy sequence of Algeria has a preferred model that is strongly sub-Poissonian, with

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Gale Document Number: GALE|A673407297