Data-driven epidemic simulation helps better policymaking. Compared with macro-scale simulations driven by statistical data, individual-level GPS data can afford finer and spatialized results. However, the big GPS data, usually collected from mobile phone users, cannot cover all populations. Therefore, this study proposes a Small World Model, to map the results from the "small world" (simulation with partially sampled data) to the real world. Based on the basic principles of disease transmission, this study derives two parameters: a time scaling factor to map the simulated period to the real period, and an amount scaling factor to map the simulated infected number to the real infected number. It is believed that this model could convert the simulation of the "small world" into the state of the real world, and analyze the effectiveness of different mobility restriction policies.
翻译:以数据驱动的流行病模拟有助于更好的决策。 与由统计数据驱动的宏观规模模拟相比,个人级别的全球定位系统数据可以提供更精细和空间化的结果。 然而,通常从移动电话用户收集的大型全球定位系统数据不能覆盖所有人口。 因此,本研究报告建议采用一个小世界模型,将“小世界”(模拟部分抽样数据)的结果映射到真实世界。 根据疾病传播的基本原则,本研究报告得出了两个参数:将模拟时期映射到真实时期的时间缩放系数,以及将模拟感染人数映射到真实感染人数的量缩放系数。 人们相信,这一模型可以将“小世界”的模拟转换成真实世界的状况,并分析不同的行动限制政策的有效性。