Leveraging Human Mobility Data for Efficient Parameter Estimation in Epidemic Models of COVID-19
Leveraging Human Mobility Data for Efficient Parameter Estimation in Epidemic Models of COVID-19
复制标题
DOI：
10.1109/tits.2022.3223229
复制
发表时间：
2022-11-30
影响因子：
8.5
通讯作者：
Chen, Jiming
中科院分区：
文献类型：
--
作者：
Shao, Cunqi;Wu, Mincheng;Chen, Jiming
有效预测疫情演变对疫情防控具有重要意义。之前的大量研究提出了大量的SIR变异，这些变异可以有效地捕捉COVID-19的传播特征。然而，以往研究中的参数估计方法基于流行病学调查数据，这不可避免地造成了较大的延迟。数字轨迹数据在世界范围内的普及，使得从人的流动性角度了解流行病的传播成为可能。数字化轨迹数据的主要优势在于每一刻都能反映种群的同址水平，从而可以提前预测种群的演化。我们发现，利用手机用户提供的移动数据可以估计个体之间的接触概率，从而揭示COVID-19的动态传播。具体而言，我们开发了一种估计方法来获得人类共地水平，并量化了疫情期间人类流动性的变化。然后，我们将感染率扩展到实时共定位水平，进一步预测流行病的传播，预测流行病的规模比传统方法准确得多。最后，通过预测具有不同流动性特征的疫情，应用所提出的方法评估不同非药物干预措施的定量效果。实验结果和模拟验证了我们的理论分析，为控制大流行提供了有效指导。
Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic.