ABSTRACT
We compared self-collected oral fluid swab specimens with and without clinician supervision, clinician-supervised self-collected anterior nasal swab specimens, and clinician-collected nasopharyngeal swab specimens for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Supervised oral fluid and nasal swab specimens performed similarly to clinician-collected nasopharyngeal swab specimens. No sample type could detect SARS-CoV-2 infections amongst all positive participants.
Subject(s)
COVID-19 , Humans , Nasopharynx , SARS-CoV-2 , Saliva , Specimen HandlingABSTRACT
Internet of Things (IOT) refers to all the devices which are present in the world and connected to the Internet. The aim of IOT is to collect and share data between two devices using the Internet. IOT is making tremendous progress in the past few years, and has proved itself very useful in many real life applications. It is very useful in applications which need both hardware and software. In this paper we will be introducing an IOT based Temperature monitoring system which is built using Raspberry pi computer, camera and sensors on the hardware side and python running in the backend. Temperature monitoring systems is a real time system which stores the data on a firebase cloud based realtime database. The paper will first start introducing the Temperature monitoring system and how it can be useful in covid-19 situation. Then it will give a brief idea of the components used along with its technical specifications. Moving forward, the paper will explain why cloud services are necessary in IOT. It will then give a brief introduction of the firebase cloud based realtime database and end that section explaining where and how we will use firebase cloud based realtime database in Temperature monitoring system. Then the paper will show a block diagram where all the input-output components are shown. It will be followed by a flowchart which will give a clear idea of how the system will work. © 2021 IEEE.
ABSTRACT
Artificial Intelligence is making tremendous progress in the past few years, Especially in the Medical Sector. The paper mainly focuses on the ways X-ray machines have been integrated with AI and how AI detects the X-rays and predicts the results correctly. The paper will begin with a brief introduction of why this is the right time to try hands in AI and how it will help the radiologists in easing their lives. The paper will then explain how Artificial Intelligence can be implemented in X-rays using Machine learning techniques and Deep learning algorithms. Moving forward, the paper will then explain about the existing systems made by companies or universities where each system has their own unique speed, features and advantages/disadvantages. Further the paper will inform how AI can help and reduce the risk of social contacting while testing for the COVID test.
ABSTRACT
The rapid development in digital infrastructure such as computing power and less expensive mobile devices and explosive growth of the internet has made an impact on digital processing. This revolution has bought many new applications and new technologies, now aiming at replacement with improving effectiveness and efficiency of manual contact tracing with new approaches with maintaining user privacy. The successful containment of the Coronavirus pandemic (COVID-19) depends on the ability to identify quickly and reliably those who have been in close proximity to a contagious positive-tested individual. This can be made possible with contact tracing method. The information about interaction which happens between two users should be revealed only to themselves to maintain privacy of individuals. Users which are detected positive should not share any of their contact details or personal information or history with the authority or any other party but pass the anonymous IDs and help in tracing out the contacted people. This paper presents the most popular contact tracing data privacy techniques. © 2020 IEEE.