使用英国威尔士国家报告数据和废水信息监测严重急性呼吸系统综合征冠状病毒2型。

IF 3.5 Q1 HEALTH CARE SCIENCES & SERVICES JMIR infodemiology Pub Date : 2023-11-23 DOI:10.2196/43891
Jordan P Cuff, Shrinivas Nivrutti Dighe, Sophie E Watson, Rafael A Badell-Grau, Andrew J Weightman, Davey L Jones, Peter Kille
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引用次数: 0

摘要

背景:新冠肺炎大流行需要对流行病学数据进行快速实时监测,以向政府和公众提供建议,但这些数据的准确性取决于无数辅助假设,尤其是公众对病例的准确报告。废水监测已在国际上成为一种准确客观的评估疾病流行率的手段,减少了延迟,减少了对公众警惕性、可靠性和参与度的依赖。然而,公众利益与新冠肺炎个人检测数据和废水监测的一致性非常差。目的:本研究评估了与新冠肺炎相关的互联网搜索量数据、公共医疗统计数据和英国南威尔士全国范围内对SARS-CoV-2的废水监测之间的关联,以调查对该流行病的兴趣如何反映国家检测和废水监测检测到的SARS-CoV-2的流行,以及如何使用这些数据来预测病例数。方法:从谷歌趋势中提取与新冠肺炎大流行相关的搜索词的相对搜索量数据,并使用多元线性模型、相关性分析和线性模型预测,将其与政府报告的新冠肺炎统计数据和英国南威尔士废水中产生的RT-qPCR SARS-CoV-2数据进行比较。结果:废水监测和信息监测都显示出流行病学监测的潜力,但其效果会随着时间的推移而变化。在整个研究期间,围绕新冠肺炎大流行的谷歌搜索量有所下降,这表明公众兴趣的减少,这可能反映在自我检测和报告量减少,随后国家报告数据的准确性下降。结论:废水监测为评估人群水平的严重急性呼吸系统综合征冠状病毒2型流行率提供了一种有价值的手段,可以与其他数据类型(如信息)相结合,以更准确地推断病毒流行率。作为客观评估严重急性呼吸系统综合征冠状病毒2型流行率的一种手段,这种监测的重要性越来越明显,以规避公众的动态兴趣和参与。与其他国家数据一样,增加公众获得废水监测数据的机会,可能会加强公众对这些监测形式的参与。临床试验:
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Monitoring SARS-CoV-2 Using Infoveillance, National Reporting Data, and Wastewater in Wales, United Kingdom: Mixed Methods Study.

Background: The COVID-19 pandemic necessitated rapid real-time surveillance of epidemiological data to advise governments and the public, but the accuracy of these data depends on myriad auxiliary assumptions, not least accurate reporting of cases by the public. Wastewater monitoring has emerged internationally as an accurate and objective means for assessing disease prevalence with reduced latency and less dependence on public vigilance, reliability, and engagement. How public interest aligns with COVID-19 personal testing data and wastewater monitoring is, however, very poorly characterized.

Objective: This study aims to assess the associations between internet search volume data relevant to COVID-19, public health care statistics, and national-scale wastewater monitoring of SARS-CoV-2 across South Wales, United Kingdom, over time to investigate how interest in the pandemic may reflect the prevalence of SARS-CoV-2, as detected by national testing and wastewater monitoring, and how these data could be used to predict case numbers.

Methods: Relative search volume data from Google Trends for search terms linked to the COVID-19 pandemic were extracted and compared against government-reported COVID-19 statistics and quantitative reverse transcription polymerase chain reaction (RT-qPCR) SARS-CoV-2 data generated from wastewater in South Wales, United Kingdom, using multivariate linear models, correlation analysis, and predictions from linear models.

Results: Wastewater monitoring, most infoveillance terms, and nationally reported cases significantly correlated, but these relationships changed over time. Wastewater surveillance data and some infoveillance search terms generated predictions of case numbers that correlated with reported case numbers, but the accuracy of these predictions was inconsistent and many of the relationships changed over time.

Conclusions: Wastewater monitoring presents a valuable means for assessing population-level prevalence of SARS-CoV-2 and could be integrated with other data types such as infoveillance for increasingly accurate inference of virus prevalence. The importance of such monitoring is increasingly clear as a means of objectively assessing the prevalence of SARS-CoV-2 to circumvent the dynamic interest and participation of the public. Increased accessibility of wastewater monitoring data to the public, as is the case for other national data, may enhance public engagement with these forms of monitoring.

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