Dataset Information

Regressing SARS-CoV-2 Sewage Measurements Onto COVID-19 Burden in the Population: A Proof-of-Concept for Quantitative Environmental Surveillance.

ABSTRACT: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an RNA virus, a member of the coronavirus family of respiratory viruses that includes severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) and the Middle East respiratory syndrome (MERS). It has had an acute and dramatic impact on health care systems, economies, and societies of affected countries during the past 8 months. Widespread testing and tracing efforts are being employed in many countries in attempts to contain and mitigate this pandemic. Recent data has indicated that fecal shedding of SARS-CoV-2 is common and that the virus RNA can be detected in wastewater. This indicates that wastewater monitoring may provide a potentially efficient tool for the epidemiological surveillance of SARS-CoV-2 infection in large populations at relevant scales. In particular, this provides important means of (i) estimating the extent of outbreaks and their spatial distributions, based primarily on in-sewer measurements, (ii) managing the early-warning system quantitatively and efficiently, and (iii) verifying disease elimination. Here we report different virus concentration methods using polyethylene glycol (PEG), alum, or filtration techniques as well as different RNA extraction methodologies, providing important insights regarding the detection of SARS-CoV-2 RNA in sewage. Virus RNA particles were detected in wastewater in several geographic locations in Israel. In addition, a correlation of virus RNA concentration to morbidity was detected in Bnei-Barak city during April 2020. This study presents a proof of concept for the use of direct raw sewage-associated virus data, during the pandemic in the country as a potential epidemiological tool.

SUBMITTER: Bar-Or I

PROVIDER: S-EPMC8762221 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionIn orbital reconstruction, the optimal location of a predefined implant can be planned preoperatively. Surgical results can be assessed intraoperatively or postoperatively. A novel method for quantifying orbital implant position is introduced. The method measures predictability of implant placement: transformation parameters between planned and resulting implant position are quantified.MethodsThe method was tested on 3 human specimen heads. Computed Tomography scans were acquired at baseline with intact orbits (t0), after creation of the defect (t1) and postoperatively after reconstruction of the defect using a preformed implant (t2). Prior to reconstruction, the optimal implant position was planned on the t0 and t1 scans. Postoperatively, the planned and realized implant position were compared. The t0 and t2 scans were fused using iPlan software and the resulting implant was segmented in the fused t2 scan. An implant reference frame was created (Orbital Implant Positioning Frame); the planned implant was transformed to the reference position using an Iterative Closest Point approach. The segmentation of the resulting implant was also registered on the reference position, yielding rotational (pitch, yaw, roll) as well as translational parameters of implant position.ResultsMeasurement with the Orbital Implant Positioning Frame proved feasible on all three specimen. The positional outcome provided more thorough and accurate insight in resulting implant position than could be gathered from distance measurements alone. Observer-related errors were abolished from the process, since the method is largely automatic.ConclusionA novel method of quantifying surgical outcome in orbital reconstructive surgery was presented. The presented Orbital Implant Positioning Frame assessed all parameters involved in implant displacement. The method proved to be viable on three human specimen heads. Clinically, the method could provide direct feedback intraoperatively and could improve postoperative evaluation of orbital reconstructive surgery.

Dataset Information

Regressing SARS-CoV-2 Sewage Measurements Onto COVID-19 Burden in the Population: A Proof-of-Concept for Quantitative Environmental Surveillance.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets