ABSTRACT
Background: The COVID-19 pandemic has affected millions of people around the world. Part of control strategies is testing a large proportion of the population to identify and isolate the infected sub-jects. Aim(s): To evaluate the SARS-CoV-2 detection by the performance of a reverse transcription and quantitative polymerase chain reaction (RT-qPCR) against SARS-CoV-2, using saliva as a matrix compared to a nasopharyngeal swab (NPS) to simplify obtaining a diagnostic sample. Method(s): Adults in outpatient care were recruited, 95% of them symptomatic. We studied 530 paired saliva and NPS samples by SARS-CoV-2 RT-qPCR. Result(s): Fifty-nine individuals tested positive in NPS and 54 in saliva samples. Sensitivity for saliva sample was 91%, specificity 100%, positive predictive value (PPV) 100%, negative predictive value (NPV) 98%. The Kappa index was 0.95 and LR-0.08. On average, the cycle threshold (CT) of saliva was 3.99 points higher than those of NPS (p < 0.0001) showing that viral load (VL) is lower in saliva than in NPS. Viral load in both decreased over the time after onset of symptoms. Saliva sampling was preferred by subjects instead of NPS. Conclusion(s): This study demonstrates that SARS-CoV-2 RT-qPCR using saliva, even with lower VL, is suitable for the diagnosis of COVID-19 in outpatient adults, especially at early stage of symptoms. Copyright © 2022, Sociedad Chilena de Infectologia. All rights reserved.
Subject(s)
COVID-19 , Pandemics , Phototherapy , COVID-19/epidemiology , COVID-19/prevention & control , Disinfection/methods , Equipment Contamination , Hand Hygiene , Humans , Occupational Diseases/prevention & control , Personal Protective Equipment , Phototherapy/instrumentation , SARS-CoV-2/radiation effects , Spain/epidemiology , Ultraviolet RaysSubject(s)
COVID-19/epidemiology , Dermatology/standards , SARS-CoV-2 , Skin/radiation effects , Sunlight , Venereology/standards , Academies and Institutes , Erythema/etiology , Humans , Quarantine , Seasons , Skin/metabolism , Spain/epidemiology , Sunburn/etiology , Sunburn/prevention & control , Vitamin D/biosynthesisABSTRACT
In times of health crises disease situation awareness is critical in the prevention and containment of the disease. One indicator for the development of many contagious diseases is the presence of fever and the proposed system, IRFIS, extends prior research into fever detection via infrared imaging in two key ways. Firstly, the system utilizes a modern, machine learning based object detection model for detecting heads, supplanting the traditional methods that relied upon shape matching. Secondly, IRFIS is capable of running from the Android mobile platform using a small, commercial-grade infrared camera. IRFIS's head detection model when evaluated on a dataset of unseen images, achieved an AP of 96.7% with an IoU of 0.50 and an AR of 75.7% averaged over IoU values between 0.50 and 0.95. IRFIS calculates the target's maximum temperature in the detected head sub-image and real results are presented as well as avenues of future work are explored. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
ABSTRACT
Responding to health crises requires the deployment of accurate and timely situation awareness. Understanding the location of geographical risk factors could assist in preventing the spread of contagious diseases and the system developed, Covid ID, is an attempt to solve this problem through the crowd sourcing of machine learning sensor-based health related detection reports. Specifically, Covid ID uses mobile-based Computer Vision and Machine Learning with a multi-faceted approach to understanding potential risks related to Mask Detection, Crowd Density Estimation, Social Distancing Analysis, and IR Fever Detection. Both visible-spectrum and LWIR images are used. Real results for all modules are presented along with the developed Android Application and supporting backend. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.