Zeng Chengbo, Zhang Jiajia, Li Zhenlong, Sun Xiaowen, Olatosi Bankole, Weissman Sharon, Li Xiaoming
South Carolina SmartState Center for Healthcare Quality, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA.
medRxiv. 2021 Jan 8:2021.01.02.21249119. doi: 10.1101/2021.01.02.21249119.
Population mobility is closely associated with coronavirus 2019 (COVID-19) transmission, and it could be used as a proximal indicator to predict future outbreaks, which could inform proactive non-pharmaceutical interventions for disease control. South Carolina (SC) is one of the states which reopened early and then suffered from a sharp increase of COVID-19.
To examine the spatial-temporal relationship between population mobility and COVID-19 outbreaks and use population mobility to predict daily new cases at both state- and county- levels in SC.
This longitudinal study used disease surveillance data and Twitter-based population mobility data from March 6 to November 11, 2020 in SC and its top five counties with the largest number of cumulative confirmed cases. Daily new case was calculated by subtracting the cumulative confirmed cases of previous day from the total cases. Population mobility was assessed using the number of users with travel distance larger than 0.5 mile which was calculated based on their geotagged twitters. Poisson count time series model was employed to carry out the research goals.
Population mobility was positively associated with state-level daily COVID-19 incidence and those of the top five counties (i.e., Charleston, Greenville, Horry, Spartanburg, Richland). At the state-level, final model with time window within the last 7-day 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-, 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% to 74.5%.
Population mobility was positively associated with COVID-19 incidences at both state- and county- levels in SC. Using Twitter-based mobility data could provide acceptable prediction for COVID-19 daily new cases. Population mobility measured via social media platform could inform proactive measures and resource relocations to curb disease outbreaks and their negative influences.
人口流动与2019冠状病毒病（COVID-19）传播密切相关，可作为预测未来疫情暴发的近期指标，为疾病控制的前瞻性非药物干预措施提供依据。南卡罗来纳州（SC）是较早重新开放的州之一，随后COVID-19病例急剧增加。
研究南卡罗来纳州人口流动与COVID-19疫情之间的时空关系，并利用人口流动预测该州及各县的每日新增病例。
这项纵向研究使用了2020年3月6日至11月11日南卡罗来纳州及其累计确诊病例数最多的五个县的疾病监测数据和基于推特的人口流动数据。每日新增病例通过用当日总病例数减去前一日累计确诊病例数来计算。利用根据带有地理标记的推特计算出的出行距离大于0.5英里的用户数量来评估人口流动情况。采用泊松计数时间序列模型来实现研究目标。
人口流动与该州及五个县（即查尔斯顿、格林维尔、霍里、斯巴达堡、里奇兰）的每日COVID-19发病率呈正相关。在州层面，过去7天时间窗口的最终模型预测误差最小，对未来3天、7天、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、霍里、斯巴达堡和里奇兰县中，分别根据过去9天、14天、28天、20天和9天的观察结果建立了最佳预测模型。14天的预测准确率在60.3%至74.5%之间。
在南卡罗来纳州，人口流动与州和县两级的COVID-19发病率呈正相关。利用基于推特的流动数据可为COVID-19每日新增病例提供可接受的预测。通过社交媒体平台衡量的人口流动可为遏制疾病暴发及其负面影响的前瞻性措施和资源重新调配提供依据。
