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

07/23/2020
by   Boudewijn F. Roukema, et al.
0

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 unusual by being anti-clustered. We adopt a one-parameter model of ϕ_i' infections per cluster, dividing any daily count n_i into n_i/ϕ_i' 'clusters', for 'country' i. We assume that n_i/ϕ_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 ϕ_i that minimises the Kolmogorov–Smirnov distance from a uniform distribution. We investigate the (ϕ_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. All are consistent with the ϕ_i model. Clustering increases with total infection count for the full sequences: ϕ_i ∼√(N_i). 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 sequences of DZ, BY, TR, AE have strongly sub-Poissonian preferred models, with ϕ_i^28 <0.5; and FI, SA, RU, AL, IR have ϕ_i^28 <3.0. Independent verification may be warranted for those countries with unusually low clustering.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset

Sign in with Google

×

Use your Google Account to sign in to DeepAI

×

Consider DeepAI Pro