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

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 classify the official national SARS-CoV-2 check for that are unusually anti-clustered. We adopt a one-parameter model $\phi _i^{\prime}$ infections per cluster, dividing any count n i into $n_i/ ‘clusters’, ‘country’ . assume ${n_i}/\phi on given day j is drawn from Poisson distribution whose mean robustly estimated four neighbouring days, calculate inferred probability $P_{ij}^{\prime}$ observation. values uniformly distributed. find value $\phi_i$ minimises Kolmogorov–Smirnov distance uniform distribution. investigate ( ϕ , N ) distribution, total consider consecutive sequences above threshold 50 infections. most inconsistent with Poissonian model. Most found consistent 28-, 14- 7-day least noisy several countries best modelled as sub-Poissonian, suggesting distinct family. 28-day sequence Algeria has preferred strongly _i^{28} < 0.1$. Tajikistan, Turkey, Russia, Belarus, Albania, United Arab Emirates Nicaragua have models also 0.5$. A statistically significant P τ 0.05) correlation was between lack media freedom country, represented by high Reporters sans frontieres Press Freedom Index (PFI 2020 ), statistical country’s counts. appears effective detector suspiciously low