论文标题
国家SARS-COV-2每日感染计数中的抗群集
Anti-clustering in the national SARS-CoV-2 daily infection counts
论文作者
论文摘要
由于固有的流行病学和管理聚类，流行病的每日感染计数中的噪声应为超级波多州。在这里，我们使用此聚类来对官方的国家SARS-COV-2每日感染计数进行分类，并检查异常抗聚类的感染计数。我们采用一个单参数型号的$ ϕ'_i $感染，每个群集，将任何每日计数$ n_i $分为$ n_i/ϕ'_i $'clusters'，用于“ country” $ i $。我们假设在给定的一天$ j $的$ n_i/ϕ'_i $是从泊松分布中绘制的，该分布的平均值是从四个相邻天估计的，并计算了观测的推断泊松概率$ p'_ _ {ij} $。 $ p'_ {ij} $值应均匀分布。我们发现将Kolmogorov-Smirnov距离与均匀分布的距离最小化的值$ ϕ_i $。我们调查了$（ϕ_i，n_i）$分布，用于总感染计数$ n_i $。我们发现，大多数每日感染计数序列与泊松模型不一致。发现大多数与$ ϕ_i $型号一致。几个国家的28，14天和7天最小噪声序列最好地建模为少oissonian，这表明一个独特的流行病学家族。阿尔及利亚的28天最小嘈杂序列具有强烈的次佛森尼亚语的首选模型，其中$ ϕ_i^{28} <0.1 $。 TJ，Tr，Ru，by，Al，Ae和Ni更喜欢也是subpoissonian的模型，带有$ ϕ_i^{28} <0.5 $。在一个国家缺乏媒体自由之间存在统计学意义（$ p^τ<0.05 $）的相关性，这是由较高的记者Sans Fronteres Press Freedom指数（PFI $^{2020} $）代表的，以及该国每日计数中缺乏统计噪音。 $ ϕ_i $模型似乎是国家SARS-COV-2每日感染计数中可疑低统计噪声的有效检测器。
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 $ϕ'_i$ 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 find that most of the daily infection count sequences are inconsistent with a Poissonian model. Most are found to be consistent with the $ϕ_i$ 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 $ϕ_i^{28} < 0.1$. TJ, TR, RU, BY, AL, AE, and NI have preferred models that are also sub-Poissonian, with $ϕ_i^{28} < 0.5$. A statistically significant ($P^τ < 0.05$) 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 $ϕ_i$ model appears to be an effective detector of suspiciously low statistical noise in the national SARS-CoV-2 daily infection counts.
