新发病毒引发的人类传染病往往具有独特的致病机理、感染状态、传播方式等流行病学多维特征，病毒在人群中的传播具有与群体接触模式强相关的特征，当前已知的传染病模型由于缺乏病毒特性的细粒度刻画和全局的群体接触行为的数据，难以解决防疫措施评估的精准性和时效性等挑战。针对上述挑战，本项目从流行病学特性和群体接触特性出发，在实际采集的大规模群体接触数据的基础上，开展包含多维异构感染特征和适应不同传播阶段的传播动力学模型研究，并以新型冠状病毒为实证研究对象验证模型有效性。针对基于移动应用程序的接触追踪和预警干扰，建立传播模型评估新型隔离筛查方式的有效性。本课题从流行病学特性、群体接触特性和防疫作用机理三个方面来研究病毒传播动力学，为最优化防控措施的制定提供基础理论和数据支撑。

Human infectious diseases caused by novel viruses often show unique epidemiological characteristics on pathogenic mechanism, infection status, and modes of transmission. The spread of viruses within human population exhibits strong correlations with contact patterns. Due to the lack of fine-grained description on epidemiological behaviors and macroscopic data on sociological behaviors, traditional epidemic models face challenges of accurate and prompt evaluation for containment measures. To tackle these challenges, we investigate fine-grained and adaptive epidemic modeling based on the epidemiological and sociological behaviors extracted from large-scale contact data. Effectiveness of the proposed model is validated by employing the novel coronavirus as a case study. To further evaluate emerging containment measures, we explore the epidemic spreading under APP based incubation screening. In summary, we investigate dynamics of epidemic spreading from aspects of epidemiological characteristics, contact behaviors and emerging means for quarantine, providing analytical and empirical support for the optimization of epidemic control and prevention.