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基于出租车出行数据的新冠疫情发病率的提前监测:感染率测量方法

Advance Monitoring of COVID-19 Incidence Based on Taxi Mobility: The Infection Ratio Measure.

作者信息

Aguilar-Ruiz Jesus S, Ruiz Roberto, Giráldez Raúl

机构信息

School of Engineering, Pablo de Olavide University, 41013 Seville, Spain.

出版信息

Healthcare (Basel). 2024 Feb 21;12(5):517. doi: 10.3390/healthcare12050517.

Abstract

The COVID-19 pandemic has had a profound impact on various aspects of our lives, affecting personal, occupational, economic, and social spheres. Much has been learned since the early 2020s, which will be very useful when the next pandemic emerges. In general, mobility and virus spread are strongly related. However, most studies analyze the impact of COVID-19 on mobility, but not much research has focused on analyzing the impact of mobility on virus transmission, especially from the point of view of monitoring virus incidence, which is extremely important for making sound decisions to control any epidemiological threat to public health. As a result of a thorough analysis of COVID-19 and mobility data, this work introduces a novel measure, the Infection Ratio (IR), which is not sensitive to underestimation of positive cases and is very effective in monitoring the pandemic's upward or downward evolution when it appears to be more stable, thus anticipating possible risk situations. For a bounded spatial context, we can infer that there is a significant threshold in the restriction of mobility that determines a change of trend in the number of infections that, if maintained for a minimum period, would notably increase the chances of keeping the spread of disease under control. Results show that IR is a reliable indicator of the intensity of infection, and an effective measure for early monitoring and decision making in smart cities.

摘要

新冠疫情对我们生活的各个方面都产生了深远影响,波及个人、职业、经济和社会领域。自2020年代初以来,我们学到了很多东西,这些知识在下次疫情出现时将非常有用。一般来说,流动性和病毒传播密切相关。然而,大多数研究分析的是新冠疫情对流动性的影响,而很少有研究专注于分析流动性对病毒传播的影响,尤其是从监测病毒发病率的角度,这对于做出合理决策以控制对公众健康的任何流行病学威胁极为重要。通过对新冠疫情和流动性数据的深入分析,这项工作引入了一种新的指标,即感染率(IR),它对阳性病例的低估不敏感,并且在疫情看似较为稳定时,对于监测疫情的上升或下降趋势非常有效,从而能够预测可能的风险情况。对于有限的空间范围,我们可以推断,在流动性限制方面存在一个显著的阈值,该阈值决定了感染数量的趋势变化,如果维持一段最短时间,将显著增加控制疾病传播的机会。结果表明,感染率是感染强度的可靠指标,也是智慧城市早期监测和决策的有效措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2b14/10930786/416aee16b081/healthcare-12-00517-g001.jpg

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