在2020年,COVID-19疫情引起全世界的关注,政府宣布了一系列非药物干预措施去遏制社会活动对传播的影响.各国不同力度的政策带来了相异的结果.为了评估这些行动的有效性,量化移动效应成为了关键问题.改变人群活动后,传播率是变化的且难以计算这种变化.因此,本文以一些欧洲国家为研究对象,收集各个国家在一些时期的人群移动情况以及每日的新增数据,并提出了流动-易感-暴露-感染-恢复(M-SEIR)模型.与SEIR模型不同,M-SEIR模型中加入了一个量化控制措施影响的变量σ(t).采用随机抽样得到初始不同状态的人群数,对模型进行迭代.使用迭代-集成卡尔曼滤波技术(IF-EAKF)对后续的迭代结果进行调整,最后得到参数的变化趋势以及每日新增的估计值.在拟合部分,设置第一轮爆发为实验期,重复100次.它的拟合结果证实了模型的可行性和稳健性.此外,这项研究对受第二轮大流行影响的欧洲国家做出了合理的预测.通过调控政策的力度以及生效时间点,本文预测了非药物措施对流行病的影响,这为未来相关政策的部署提供了参考.最后,剔除人群移动、气温等外部因素后,研究得到了一个有趣的发现:尽管第三轮的每日报告远高于第一轮,但是第三轮的病毒传播参数要低于第一轮,进一步考察发现该下降与疫苗接种相关.“,”COVID-19 pandemic captured the full attention of the world in 2020, and the government declared a series of non-pharmacological interventions ( NPIs) to curb the influence of social movement on transmission. In different countries, different policies bring about different results. Quantifying the effect of the movement becomes a vital issue for evaluating the effectiveness of these actions. The transmission rate changes and is hard to computer after altering activity. Therefore, this research sets some European countries as the research objects, collects mobility data and daily cases during some periods, and proposes a mobility-susceptible-exposure-infectious-recovery ( M-SEIR) model. Unlike the SEIR model, the movement change is quantified as a variable (σ( t) ) and added in the M-SEIR model. With random sampling to get the number of people in different initial states, this research iterates the model. The iterative filtering ensemble adjustment Kalman filter ( IF-EAKF ) is used to adjust the subsequent iterative results. In the research, it receives the changing trend of parameters and the daily new estimation in the end. Set the first round as the fitting period and repeat the experiment 100 times in the fitting part. The result confirms the feasibility and robustness of the model. In addition, this study makes a reasonable forecast for European countries about the second round. By controlling the strength and the time point of applying non-pharmacological interventions, the research predicts the impact of these actions on the pandemic and provides some suggestions for the deployment of relevant policies in the future. Finally the study eliminates the external factors such as motion and temperature, and obtains an interesting discovery:Despite the daily case in the third round higher than that in the first round, the transmission parameter in the former appears lower than that in the latter. The further survey shows that it might be related to vaccination.