论文标题
在COVID-19活动相关网络中检测全球社区结构
Detecting Global Community Structure in a COVID-19 Activity Correlation Network
论文作者
论文摘要
在过去的2。5年中，Covid-19的全球大流行产生了大量的流行/公共卫生数据集，这对于研究我们全球联系世界的基础结构也可能很有用。在这里，我们使用约翰·霍普金斯大学Covid-19数据集构建了国家/地区的相关网络，并研究了其全球社区结构。具体而言，我们选择了来自数据集中至少有100,000个累积阳性案例的国家/地区，并为每个国家/地区报告的新阳性案例产生了7天移动的平均时间序列。然后，我们通过在新正案例数量的日志上进行日常差异来计算每日变更指数的时间序列。我们通过将其每日变化指数时间序列具有正相关的国家/地区连接到使用Pearson相关系数作为边缘的重量来构建了一个相关网络。应用模块化最大化方法表明，有三个主要社区：（1）欧洲 +北美 +东南亚 +东南亚在大流行期间显示出类似的六峰模式，（2）主要是近/中东 +中东/中东/南亚 +中部/南美/南美，它们宽松地跟随社区1，但由于delta的变化而显着及其远的活动。加拿大 +澳大利亚直到Omicron变体引起的巨大尖峰才有太多活动。这三个社区在各种环境下被强烈检测到。通过在这三个群落中使用中位曲线来构建3D“相空间”，用于X-Y-Z坐标产生了一个有效的摘要轨迹，以了解全球大流行的进展方式。
The global pandemic of COVID-19 over the last 2.5 years have produced an enormous amount of epidemic/public health datasets, which may also be useful for studying the underlying structure of our globally connected world. Here we used the Johns Hopkins University COVID-19 dataset to construct a correlation network of countries/regions and studied its global community structure. Specifically, we selected countries/regions that had at least 100,000 cumulative positive cases from the dataset and generated a 7-day moving average time series of new positive cases reported for each country/region. We then calculated a time series of daily change exponents by taking the day-to-day difference in log of the number of new positive cases. We constructed a correlation network by connecting countries/regions that had positive correlations in their daily change exponent time series using their Pearson correlation coefficient as the edge weight. Applying the modularity maximization method revealed that there were three major communities: (1) Mainly Europe + North America + Southeast Asia that showed similar six-peak patterns during the pandemic, (2) mainly Near/Middle East + Central/South Asia + Central/South America that loosely followed Community 1 but had a notable increase of activities because of the Delta variant and was later impacted significantly by the Omicron variant, and (3) mainly Africa + Central/East Canada + Australia that did not have much activities until a huge spike was caused by the Omicron variant. These three communities were robustly detected under varied settings. Constructing a 3D "phase space" by using the median curves in those three communities for x-y-z coordinates generated an effective summary trajectory of how the global pandemic progressed.
