Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis.
Spatial-Temporal Relationship Between Population Mobility and COVID-19 Outbreaks in South Carolina: Time Series Forecasting Analysis.
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DOI：
10.2196/27045
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发表时间：
2021-04-13
影响因子：
7.4
通讯作者：
Li X
中科院分区：
文献类型：
--
作者：
Zeng C;Zhang J;Li Z;Sun X;Olatosi B;Weissman S;Li X
人口流动性与COVID-19传播密切相关，它可以用作预测未来疫情的近端指标，这可以为疾病控制的主动非药物干预提供信息。南卡罗来纳州是美国较早重新开放的州之一，随后它经历了COVID-19病例的急剧增加。本研究旨在研究人口流动与COVID-19疫情之间的时空关系，并利用人口流动数据预测南卡罗来纳州州和县一级的每日新增病例。这项纵向研究使用了2020年3月6日至11月11日期间南卡罗来纳州及其五个累计确诊COVID-19病例最多的县的疾病监测数据和基于Twitter的人口流动数据。人口流动性是根据旅行距离大于0.5英里的Twitter用户数量进行评估的。采用泊松计数时间序列模型进行COVID-19预测。人口流动性与州级每日COVID-19发病率以及前五名县（即查尔斯顿、格林维尔、霍利、斯帕坦堡和里奇兰）的发病率呈正相关。在州一级，时间窗口在最近7天内的最终模型预测误差最小，对未来3、7和14天的预测准确率分别高达98.7%、90.9%和81.6%。在查尔斯顿、格林维尔、霍利、斯帕坦堡和里奇兰县中，分别根据过去9、14、28、20和9天的观测结果建立了最佳预测模型。14天预测准确率为60.3%-74.5%。使用基于Twitter的人口流动数据可以提供南卡罗来纳州州和县一级COVID-19每日新增病例的可接受预测。通过社交媒体数据衡量的人口流动情况可以为采取积极措施和资源转移提供信息，以遏制疾病爆发及其负面影响。
Population mobility is closely associated with COVID-19 transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive nonpharmaceutical interventions for disease control. South Carolina is one of the US states that reopened early, following which it experienced a sharp increase in COVID-19 cases. The aims of this study are to examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility data to predict daily new cases at both the state and county level in South Carolina. This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020, in South Carolina and its five counties with the largest number of cumulative confirmed COVID-19 cases. Population mobility was assessed based on the number of Twitter users with a travel distance greater than 0.5 miles. A Poisson count time series model was employed for COVID-19 forecasting. Population mobility was positively associated with state-level daily COVID-19 incidence as well as incidence in the top five counties (ie, Charleston, Greenville, Horry, Spartanburg, and Richland). At the state level, the final model with a time window within the last 7 days had the smallest prediction error, and the prediction accuracy was as high as 98.7%, 90.9%, and 81.6% for the next 3, 7, and 14 days, respectively. Among Charleston, Greenville, Horry, Spartanburg, and Richland counties, the best predictive models were established based on their observations in the last 9, 14, 28, 20, and 9 days, respectively. The 14-day prediction accuracy ranged from 60.3%-74.5%. Using Twitter-based population mobility data could provide acceptable predictions of COVID-19 daily new cases at both the state and county level in South Carolina. Population mobility measured via social media data could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.
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DOI：
10.1016/j.mayocpiqo.2020.10.003
发表时间：
2020-12
期刊：
Mayo Clinic proceedings. Innovations, quality & outcomes
影响因子：
--
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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30.8
作者：
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作者：
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