MIVEGEC, University of Montpellier, CNRS, IRD, Montpellier, France.
Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK; Oxford University Clinical Research Unit, Ho Chi Minh City, Vietnam.
Int J Infect Dis. 2021 Oct;111:100-107. doi: 10.1016/j.ijid.2021.08.029. Epub 2021 Aug 14.
Background COVID-19 was first detected in Wuhan, China, in 2019 and spread worldwide within a few weeks. The COVID-19 epidemic started to gain traction in France in March 2020. Subnational hospital admissions and deaths were then recorded daily and served as the main policy indicators. Concurrently, mobile phone positioning data have been curated to determine the frequency of users being colocalized within a given distance. Contrarily to individual tracking data, these can be a proxy for human contact networks between subnational administrative units. Methods Motivated by numerous studies correlating human mobility data and disease incidence, we developed predictive time series models of hospital incidence between July 2020 and April 2021. We added human contact network analytics, such as clustering coefficients, contact network strength, null links or curvature, as regressors. Findings We found that predictions can be improved substantially (by more than 50%) at both the national level and the subnational level for up to 2 weeks. Our subnational analysis also revealed the importance of spatial structure, as incidence in colocalized administrative units improved predictions. This original application of network analytics from colocalization data to epidemic spread opens new perspectives for epidemic forecasting and public health.
背景 2019 年，COVID-19 首先在中国武汉被发现，并在几周内迅速传播到全球。2020 年 3 月，COVID-19 疫情开始在法国蔓延。此后，每日记录省级医院的入院和死亡人数，作为主要政策指标。同时，还整理了手机定位数据，以确定用户在给定距离内同时出现的频率。与个人追踪数据不同，这些数据可以作为省级行政单位之间人际接触网络的替代指标。 方法 受大量将人类流动性数据与疾病发病率相关联的研究启发，我们开发了 2020 年 7 月至 2021 年 4 月期间省级医院发病率的预测时间序列模型。我们还添加了人际接触网络分析，如聚类系数、接触网络强度、空链接或曲率作为回归量。 结果 我们发现，在全国和省级层面上，预测结果可以显著提高（超过 50%），预测时间最长可达 2 周。我们的省级分析还揭示了空间结构的重要性，因为同时出现的行政单位的发病率提高了预测效果。这种从共定位数据到疫情传播的网络分析的原始应用，为疫情预测和公共卫生开辟了新的视角。
