交通分析区尺度上的COVID-19时空扩散推估方法:以武汉市为例

Traffic Analysis Zone-Based Epidemic Estimation Approach of COVID-19 Based on Mobile Phone Data:An Example of Wuhan

现有的流行病学模型大多是通过对统计数据进行拟合,实现对患病人数的推估,较少考虑细粒度空间人群移动交互对时空扩散特征的直接作用。将空间交互特征融入流行病学模型,提出了基于手机用户空间交互数据的新型冠状病毒肺炎(coronavirus disease 2019,COVID-19)时空扩散推估方法,并对2019-12-2020-03武汉市COVID-19患病人数以及时空扩散过程进行推估。研究结果表明,该方法能有效推估出每天的疫情新增交通分析区,能够完全覆盖了有疫情公告的交通分析区,且存在疫情公告的交通分析区占所推估交通分析区的72.7%;2020-02-18后的累计推估患者结果与官方公布患者总量吻合得非常好,差距约为5.6%,间接验证了前期推估的合理性。由此得出,该方法能比较有效地推估细粒度空间之间的传染病传播,对正确认识细粒度空间下人群交互对传染病时空扩散的影响机制,增强宏观流行病学模型的空间可解释性具有一定的科学意义。

Current epidemic models mainly estimate the number of confirmed patients by fitting statistical data. Few studies consider the direct effect of fine-grained spatial crowd mobile interaction on the spatial-temporal diffusion features. A new method for estimating the spatial-temporal spread process of coronavirus disease2019(COVID-19) is proposed, incorporating spatial interaction features into epidemiological models. This paper also estimates the number of confirmed patients and spatial-temporal spread process of COVID-19 in Wuhan from December 2019 to March 2020. The results show that the method proposed in this paper can effectively estimate the daily traffic analysis zones(TAZs) where new confirmed patients appear, completely covering the TAZs with the epidemic announcements. And the TAZs with the epidemic announcements account for 72.7% of the estimated TAZs. The cumulative number of estimated confirmed patients agrees very well with the total number of officially announced confirmed patients after February 18, 2020, with a gap of approximately 5.6%, indirectly verifying the rationality of the previous estimation. The method proposed in this paper can effectively estimate the spread of infectious diseases under fine-grained spaces. It also has scientific significance in understanding the influence mechanism of the crowd interaction under finegrained spaces on the spatial-temporal spread of infectious diseases, and enhancing the macroscopically spatial interpretability of epidemiological models macroscopic.