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意大利北部基于污水的 SARS-CoV-2 流行病学:时空模型。

Wastewater-Based Epidemiology for SARS-CoV-2 in Northern Italy: A Spatiotemporal Model.

机构信息

Regional Agency for Prevention, Environment and Energy of Emilia-Romagna, 40139 Bologna, Italy.

Alma Mater Institute on Healthy Planet, Department of Biological, Geological and Environmental Sciences, University of Bologna, 40138 Bologna, Italy.

出版信息

Int J Environ Res Public Health. 2024 Jun 6;21(6):741. doi: 10.3390/ijerph21060741.

DOI:10.3390/ijerph21060741
PMID:38928987
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11203876/
Abstract

The study investigated the application of Wastewater-Based Epidemiology (WBE) as a tool for monitoring the SARS-CoV-2 prevalence in a city in northern Italy from October 2021 to May 2023. Based on a previously used deterministic model, this study proposed a variation to account for the population characteristics and virus biodegradation in the sewer network. The model calculated virus loads and corresponding COVID-19 cases over time in different areas of the city and was validated using healthcare data while considering viral mutations, vaccinations, and testing variability. The correlation between the predicted and reported cases was high across the three waves that occurred during the period considered, demonstrating the ability of the model to predict the relevant fluctuations in the number of cases. The population characteristics did not substantially influence the predicted and reported infection rates. Conversely, biodegradation significantly reduced the virus load reaching the wastewater treatment plant, resulting in a 30% reduction in the total virus load produced in the study area. This approach can be applied to compare the virus load values across cities with different population demographics and sewer network structures, improving the comparability of the WBE data for effective surveillance and intervention strategies.

摘要

本研究旨在探讨污水流行病学(WBE)作为一种工具,用于监测 2021 年 10 月至 2023 年 5 月意大利北部一座城市 SARS-CoV-2 的流行情况。本研究基于先前使用的确定性模型,提出了一种变体,以考虑到污水管网中的人口特征和病毒生物降解。该模型计算了不同城市区域随时间推移的病毒载量和相应的 COVID-19 病例,并结合医疗保健数据进行了验证,同时考虑了病毒突变、疫苗接种和检测变异性。在所考虑的三个流行期间,预测病例与报告病例之间的相关性很高,表明该模型能够预测相关病例数量的波动。人口特征对预测和报告的感染率没有显著影响。相反,生物降解显著降低了到达废水处理厂的病毒载量,导致研究区域产生的总病毒载量减少了 30%。这种方法可用于比较具有不同人口统计学特征和污水管网结构的城市之间的病毒载量值,提高 WBE 数据的可比性,以制定有效的监测和干预策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/a99a9af8eac3/ijerph-21-00741-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/191b46b83d29/ijerph-21-00741-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/3d021ff818ba/ijerph-21-00741-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/28bd8278f579/ijerph-21-00741-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/98b9d5409d59/ijerph-21-00741-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/3686f523a31b/ijerph-21-00741-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/baf8103ffb80/ijerph-21-00741-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/a99a9af8eac3/ijerph-21-00741-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/191b46b83d29/ijerph-21-00741-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/3d021ff818ba/ijerph-21-00741-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/28bd8278f579/ijerph-21-00741-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/98b9d5409d59/ijerph-21-00741-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/3686f523a31b/ijerph-21-00741-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/baf8103ffb80/ijerph-21-00741-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8687/11203876/a99a9af8eac3/ijerph-21-00741-g007.jpg

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本文引用的文献

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