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

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 '_i infections per cluster, dividing any daily count n_i into n_i/'_i 'clusters', for 'country' i. We assume that n_i/'_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 _i that minimises the Kolmogorov-Smirnov distance from a uniform distribution. We investigate the (_i, N_i) distribution, for total infection count N_i. We find that most of the daily infection count sequences are inconsistent with a Poissonian model. Most are found to be consistent with the _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 _i^28 < 0.1. TJ, TR, RU, BY, AL, AE, and NI have preferred models that are also sub-Poissonian, with _i^28 < 0.5. A statistically significant (P^ < 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 (PFI^2020), and the lack of statistical noise in the country's daily counts. The _i model appears to be an effective detector of suspiciously low statistical noise in the national SARS-CoV-2 daily infection counts.

Tasks

Reproductions