Anticlustering in the national SARSCoV2 daily infection counts
Abstract
The noise in daily infection counts of an epidemic should be superPoissonian due to intrinsic epidemiological and administrative clustering. Here, we use this clustering to classify the official national SARSCoV2 daily infection counts and check for infection counts that are unusually anticlustered. We adopt a oneparameter model of phi_i' infections per cluster, dividing any daily count n_i into n_i/phi_i' 'clusters', for 'country' i. We assume that n_i/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 P_{ij}' of the observation. The P_{ij}' values should be uniformly distributed. We find the value phi_i that minimises the KolmogorovSmirnov distance from a uniform distribution. We investigate the (phi_i, N_i) distribution, for total infection count N_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_i model. The 28, 14 and 7day least noisy sequences for several countries are best modelled as subPoissonian, suggesting a distinct epidemiological family. The 28day least noisy sequence of Algeria has a preferred model that is strongly subPoissonian, with phi^{28}_i<0.1. Tajikistan, Turkey, Russia, Belarus, Albania, United Arab Emirates and Nicaragua have preferred models that are also subPoissonian, with phi^{28}_i<0.5. A statistically significant (P_tau < 0.05) correlation was found between the lack of media freedom in a country, as represented by a high Reporters sans frontieres Press Freedom Index (PFI2020), and the lack of statistical noise in the country's daily counts. The phi_i model appears to be an effective detector of suspiciously low statistical noise in the national SARSCoV2 daily infection counts.
 Publication:

PeerJ
 Pub Date:
 August 2021
 DOI:
 10.7717/peerj.11856
 arXiv:
 arXiv:2007.11779
 Bibcode:
 2021PeerJ...911856R
 Keywords:

 Quantitative Biology;
 Populations and Evolution;
 Physics;
 Physics and Society;
 Statistics;
 Methodology;
 Quantitative Biology  Populations and Evolution;
 Physics  Physics and Society;
 Statistics  Methodology
 EPrint:
 30 pages, 17 figures, 12 tables