Wastewater-based epidemiology has been widely used as a cost-effective method for tracking the COVID-19 pandemic at the community level. Here we describe COVIDBENS, a wastewater surveillance program running from June 2020 to March 2022 in the wastewater treatment plant of Bens in A Coruña (Spain). The main goal of this work was to provide an effective early warning tool based in wastewater epidemiology to help in decision-making at both the social and public health levels. RT-qPCR procedures and Illumina sequencing were used to weekly monitor the viral load and to detect SARS-CoV-2 mutations in wastewater, respectively. In addition, own statistical models were applied to estimate the real number of infected people and the frequency of each emerging variant circulating in the community, which considerable improved the surveillance strategy. Our analysis detected 6 viral load waves in A Coruña with concentrations between 10 and 10 SARS-CoV-2 RNA copies/L. Our system was able to anticipate community outbreaks during the pandemic with 8-36 days in advance with respect to clinical reports and, to detect the emergence of new SARS-CoV-2 variants in A Coruña such as Alpha (B.1.1.7), Delta (B.1.617.2), and Omicron (B.1.1.529 and BA.2) in wastewater with 42, 30, and 27 days, respectively, before the health system did. Data generated here helped local authorities and health managers to give a faster and more efficient response to the pandemic situation, and also allowed important industrial companies to adapt their production to each situation. The wastewater-based epidemiology program developed in our metropolitan area of A Coruña (Spain) during the SARS-CoV-2 pandemic served as a powerful early warning system combining statistical models with mutations and viral load monitoring in wastewater over time.
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http://dx.doi.org/10.1007/s11356-023-27877-3 | DOI Listing |
Int J Emerg Med
December 2024
Department of Emergency Medicine, Faculty of Medicine, Siriraj Hospital, Mahidol University, 2 Wanglang Road, Bangkoknoi, Bangkok, 10700, Thailand.
Background: Pneumonia is a potentially life-threatening respiratory tract infection. Many Early Warning Scores (EWS) were developed to detect patients with high risk for adverse clinical outcomes, but few have explored the utility of these EWS for pneumonia patients in the Emergency Department (ED) setting. We aimed to compare the prognostic utility of A-DROP, NEWS2, and REMS in predicting in-hospital mortality and the requirement for mechanical ventilation among ED patients with pneumonia.
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December 2024
College of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130012, China.
In the context of rapid urbanization, the proliferation of high-density residential zones and intricate infrastructure networks markedly amplifies a city's susceptibility to natural calamities, notably seismic events. Thus, a precise evaluation of a city's emergency capability for seismic events is imperative. This research proposes a novel and all-encompassing evaluation framework for indicators, grounded in crisis management theory, covering the entire spectrum of disaster mitigation, preparedness, response, and recovery.
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December 2024
College of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
This study focuses on the northern scenic area of Changbai Mountain, aiming to evaluate the emergency evacuation capacity of the region in the context of geological disasters and to formulate corresponding improvement strategies. Due to the relatively small area of this region, difficulties in data acquisition, and insufficient precision, traditional models for evaluating emergency evacuation capacity are typically applied to urban built environments, with relatively few studies addressing scenic areas. To tackle these issues, this research employs the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), which successfully resolves the problem of blurriness in remote sensing images and significantly enhances image clarity.
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December 2024
Xinjiang Irtysh River Investment and Development (Group) Co., Ltd., Wulumuqi, 830000, China.
Abnormal cutter wear has a serious impact on TBM construction. If not found in time, it may lead to the cutterhead overall failure. Aiming at this problem, a general model and method to identify and warn the abnormal cutter wear using Extreme Learning Machine (ELM) is proposed.
View Article and Find Full Text PDFJ Hazard Mater
December 2024
State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China.
Under the widespread use backgrounds of fungicides in paddy-dominated basin, the neglect of fungicide environmental fates may aggravate their pollution risks. By integrating field detection with model simulation, we quantified the loss loads and explored the environmental fates of one thiophosphate and five triazole fungicides. Based on the experimental results, we simulated fungicide loss loads with the coefficient of determination of the verification results greater than 0.
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