The COVID-19 pandemic continues to impact daily life worldwide. It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic. Here, we aim to demonstrate that it is feasible to analyze the patterns of the pandemic using a time-series clustering approach. In this work, we use dynamic time warping distance and hierarchical clustering to cluster time series of daily new cases and deaths from different countries into four patterns. It is found that geographic factors have a large but not decisive influence on the pattern of pandemic development. Moreover, the age structure of the population may also influence the formation of cluster patterns. Our proven valid method may provide a different but very useful perspective for other scholars and researchers.

The COVID‐19 pandemic spread catastrophically over the world since the spring of 2020. In this paper, a heterogeneous branching process with immigration is established to quantify the human‐to‐human transmission of COVID‐19 in local communities, based on the temporal and structural transmission patterns extracted from public case disclosures by four provincial Health Commissions in China. With proper parameter settings, our branching model matches the actual transmission chains satisfactorily and, therefore, sheds light on the underlying COVID‐19 spreading mechanism. Moreover, based on our branching model, the efficacy of home quarantine and social distancing are explored, providing a reference for the effective prevention of COVID‐19 worldwide.

The global spread of the COVID-19 pandemic is changing everything in 2020. It is of crucial importance to characterize the growth patterns during the transmission. In this paper, a generalized-growth model is established to present the evolution of the number of the total confirmed cases changing with time. Due to effective containment, the generalized growth model reveals a piecewise pattern, referred to as the sub-exponential and the sub-linear stages. Moreover, the parameters can quantify the effectiveness of the containment and the trend of resurgence in different regions all over the world. Our model provides a phenomenological approach, which is simple and transparent for better understanding of the typical patterns within the general dynamics. Our model may have implications for possible nowcasting and forecasting of the pandemic trend.

Social platforms make information propagation anywhere anytime. Large quantity data recording information spreading is available for further understanding the intrinsic mechanism within these stochastic processes. Based on the empirical spreading trees of tweets on Twitter, the heterogeneity of Twitter users is explored, turning out the burstiness in the spreading process. A few super spreaders can significantly change the trends of information spreading. Moreover, an improved Hawkes process is designed in this study to better investigate users’ heterogeneity during information propagation. Verification is provided for accuracy and stability of the improved Hawkes model in simulating propagation patterns revealed in empirical sequential data, predicting spreading trends, and predicting probability of information outbreaks. Our improved Hawkes model is an effective spreading model for detecting and quantifying super spreaders during the propagation process, which may shed light on the control and prediction of information spreading in social media.

基于安徽省卫生健康委员会截至2020年2月19日公布的800余例新型冠状病毒肺炎病例信息, 根据病例中公布的接触史构建确诊患者间的有向传播关系, 发现源传染患者中男性居多, 被传染患者中女性居多. 从病例信息中可知, 安徽省新型冠状病毒肺炎疫情的发展从初期的具有武汉居住或接触史的输入病例转入后期本地传播为主的小范围社区传播, 且严格的防控隔离措施有效切断了社区内的进一步传播. 源传染患者与被传染患者的确诊时间间隔可用Γ分布拟合, 确诊时间间隔的中位数为2 d, 平均值为2.67 d. 基于有向传播关系的统计特点, 构建安徽省疫情发展后期的自回归传播模型, 模型仿真结果与疫情发展数据符合. 对除湖北省的全国确诊病例数据同样采取自回归建模与仿真, 结果仍与疫情发展数据符合. 这一发现为控制疫情在湖北省以外区域的防控提供了参考: 通过严格的防控措施和隔离措施, 疫情在湖北省之外的传播具有很大的黏滞性, 多为家庭程度的密切接触传播, 且能有效控制新型冠状病毒肺炎在当地的传播深度, 有效控制了疫情的蔓延.