...
首页> 外文期刊>Science of the total environment >On forecasting the community-level COVID-19 cases from the concentration of SARS-CoV-2 in wastewater
【24h】

On forecasting the community-level COVID-19 cases from the concentration of SARS-CoV-2 in wastewater

机译:关于废水中SARS-COV-2集中的社区级Covid-19案件预测

获取原文
获取原文并翻译 | 示例
           

摘要

The building of an effective wastewater-based epidemiological mode! that can translate SARS-CoV-2 concentrations in wastewater to the prevalence of virus shedders within a community is a significant challenge for waste-water surveillance. The objectives of this study were to investigate the association between SARS-CoV-2 wastewater concentrations and the COVID-19 cases at the community-level and to assess how SARS-CoV-2 wastewater concentrations should be integrated into a wastewater-based epidemiological statistical model that can provide reliable forecasts for the number of COVID-19 infections and the evolution over time as well. Weekly variations on the SARS-CoV-2 wastewater concentrations and COVID-19 cases from April 29, 2020 through February 17,2021 were obtained in Borough of Indiana, PA. Vector autoregression (VAR) model with different data forms were fitted on this data from April 29,2020 through January 27,2021, and the performance in three weeks ahead forecasting (February 3,10, and 17) were compared with measures of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). A stationary block bootstrapping VAR method was also presented to reduce the variability in the forecasting values. Our results demonstrate that VAR(1) estimated with the logged data has the best interpretation of the data, but a VAR(1) estimated with the original data has a stronger forecasting ability. The forecast accuracy, measured by MAPE, for 1 week, 2 weeks, and 3 weeks in the future can be as low as 11.85%, 8.97% and 21.57%. The forecasting performance of the model on a short time span is unfortunately not very impressive. Also, a single increase in the SARS-CoV-2 concentration can impact the COVID-19 cases in an inverted-U shape pattern with the maximum impact occur in the third week after. The flexibility of this approach and easy-to-follow explanations are suitable for many different locations where the wastewater surveillance system has been implemented.
机译:建设有效的废水基流行病学模式!这可以将SARS-COV-2浓度转化为废水中的浓度,以在社区内的病毒患病者的患病率是对废水监测的重要挑战。本研究的目的是探讨SARS-COV-2废水浓度和Covid-19患者在社区一级之间的关联,并评估SARS-COV-2废水浓度如何纳入废水的流行病学统计数据可以为Covid-19感染的数量和随着时间的推移提供可靠的预测的模型。 SARS-COV-2废水浓度和Covid-19案件的每周变异,从4月29日至2月17,2021中获得PA,PA的植物区获得。传染媒介自动增加(var)具有不同数据形式的型号在2012年4月29日至2012年4月27日至2011年4月27日至2012年1月27日,并将在三周内(2月3,10和17日)的性能与平均绝对的措施进行了比较错误(MAE)和平均绝对百分比错误(MAPE)。还提出了一种静止块自举var方法以降低预测值的可变性。我们的结果表明,使用记录数据估计的VAR(1)具有最佳的数据解释,但是使用原始数据估计的VAR(1)具有更强的预测能力。以MAPE测量的预测准确性为1周,2周和3周,可以低至11.85%,8.97%和21.57%。遗憾的是,模型的预测性能不幸的是不是很令人印象深刻。此外,SARS-COV-2浓度的单一增加可以影响倒U形图案中的Covid-19例,在第三周发生最大的影响。这种方法的灵活性和易于遵循的解释适用于许多不同的位置,其中已经实施了废水监测系统。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号