Suppr超能文献

在疫情期间以及新冠病毒感染传播过程中按距离预测每日出行行为——我们处于闭环情景中吗?

Projecting daily travel behavior by distance during the pandemic and the spread of COVID-19 infections - Are we in a closed loop scenario?

作者信息

Truong Dothang, Truong My D

机构信息

Embry-Riddle Aeronautical University, United States.

University of Central Florida, United States.

出版信息

Transp Res Interdiscip Perspect. 2021 Mar;9:100283. doi: 10.1016/j.trip.2020.100283. Epub 2020 Dec 18.

Abstract

Understanding the future development of COVID-19 is the key to contain the spreading of the coronavirus. The purpose of this paper is to explore a potential relationship between United States residents' daily trips by distance and the COVID-19 infections in the near future. The study used the daily travel data from the Bureau of Transportation Statistics (BTS) and the COVID-19 data from the Centers for Disease Control and Prevention (CDC) in the United States. Time-series forecast models using Autoregressive Moving Average (ARIMA) method were constructed to project future trends of United States residents' daily trips by distance at the national level from November 30, 2020, to February 28, 2021. A comparative trend analysis was conducted to detect the patterns of daily trips and the spread of COVID-19 during that period. The results revealed a closed loop scenario, in which the residents' travel behavior dynamically changes based on their risk perception of COVID-19 in an infinite loop. A detected lag in the travel behavior between short trips and long trips further worsens the situation and creates more difficulties in finding an effective solution to break the loop. The study shed new light on efforts to contain and control the spread of the coronavirus. The loop can only be broken with proper and prompt mitigation strategies to reduce the burden on hospitals and healthcare systems and save more lives.

摘要

了解新冠病毒病(COVID-19)的未来发展是遏制冠状病毒传播的关键。本文旨在探讨美国居民按出行距离划分的日常出行与近期COVID-19感染之间的潜在关系。该研究使用了美国运输统计局(BTS)的日常出行数据以及美国疾病控制与预防中心(CDC)的COVID-19数据。构建了采用自回归移动平均(ARIMA)方法的时间序列预测模型,以预测2020年11月30日至2021年2月28日期间美国居民按出行距离划分的日常出行在全国层面的未来趋势。进行了对比趋势分析,以检测该时期内日常出行模式和COVID-19的传播情况。结果揭示了一种闭环情形,即居民的出行行为基于他们对COVID-19的风险认知在一个无限循环中动态变化。短途出行和长途出行之间检测到的出行行为滞后进一步恶化了这种情况,并给找到打破该循环的有效解决方案带来了更多困难。该研究为遏制和控制冠状病毒传播的努力提供了新的思路。只有通过适当且及时的缓解策略来减轻医院和医疗系统的负担并挽救更多生命,才能打破这个循环。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/34d8/7836771/16ecd37f7a41/gr1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验