Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing.
Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing.
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DOI：
10.1038/s41467-021-21776-2
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发表时间：
2021-03-08
影响因子：
16.6
通讯作者：
Leung GM
中科院分区：
文献类型：
--
作者：
Leung K;Wu JT;Leung GM
在持续的COVID-19大流行中，人类流动性和身体混合的数字代理已被用于监测病毒传播率和社交距离干预措施的有效性。我们开发了一个新的框架，用特定年龄的数字移动数据参数化疾病传播模型。通过将模型拟合到香港的病例数据，我们能够在接近真实的时间（即，不再受感染和报告病例之间约9天延迟的限制），这对于快速评估减少传染性干预措施的有效性至关重要。我们的研究结果表明，通过将物理混合的有效数字代理整合到传统流行病模型中，可以获得COVID-19流行病的准确临近预报和预测。人类流动性的数字代理可用于监测社交距离，因此有可能推断COVID-19的动态。在这里，作者将来自香港的旅行卡数据整合到一个传输模型中，并表明它可以用于近实时跟踪传输率。
Digital proxies of human mobility and physical mixing have been used to monitor viral transmissibility and effectiveness of social distancing interventions in the ongoing COVID-19 pandemic. We develop a new framework that parameterizes disease transmission models with age-specific digital mobility data. By fitting the model to case data in Hong Kong, we are able to accurately track the local effective reproduction number of COVID-19 in near real time (i.e., no longer constrained by the delay of around 9 days between infection and reporting of cases) which is essential for quick assessment of the effectiveness of interventions on reducing transmissibility. Our findings show that accurate nowcast and forecast of COVID-19 epidemics can be obtained by integrating valid digital proxies of physical mixing into conventional epidemic models. Digital proxies of human mobility can be used to monitor social distancing, and therefore have potential to infer COVID-19 dynamics. Here, the authors integrate travel card data from Hong Kong into a transmission model and show that it can be used to track transmissibility in near real-time.
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