Knowledge, Preventive Behaviour and Risk Perception about COVID-19 in Health Care Professional Students

With 3.95 lakhs of active COVID-19 cases in India and Tamilnadu being the second-largest hub of COVID-19, the health and social impact on the public, especially the health care warriors would be considerable. To evaluate and compare the levels of knowledge, preventive behaviour and risk perception of South Indian Health Care Professional(HCP) Students regarding COVID-19.Methods: An institution-based cross-sectional questionnaire survey was conducted in a tertiary care centre and teaching hospital in May-June 2020 amongst 873 students pursuing various HCP courses. The questionnaire comprised four sections - demographic details, COVID-19 related knowledge, preventive behaviour and risk perception. Females volunteered to complete the survey (n= 623;71.4%) more than males (n= 250;28.6%). Most participants had received awareness about COVID-19 (n=860;98.5%) from many resources of information. While females (97.50 ± 8.94) had significantly higher scores on items for preventive behaviour than males (94.7±15.55;p=0.006), the scores were similar for knowledge and risk perception. There was a statistically significant difference in risk perception among various courses with students pursuing Pharmacy having higher risk perception. Items regarding the use of masks in general and hospital setups and availability of antivirals for COVID-19 received many incorrect responses. HCP students presented with high levels of COVID-19 related knowledge and preventive behaviour, but moderate risk perception. Continuing education programs and preventive behavioural training are the need of the hour to strengthen the knowledge and alleviate the anxiety of HCP students towards the pandemic.

Design and Fabrication of Automatic Oxygen Flow Controller for COVID Patient

In this ongoing pandemic situation, an acute shortage of oxygen due to number of patients required the pure oxygen supply becoming high, showed us the level of importance of this gas. People getting affected by COVID-19 are suffering from low saturation level which needs to be increased by the supply of pure oxygen. The oxygen is used to bring the saturation level to 94%. Any drop below 84% lead to serious respiratory failures. We propose a system where the oxygen is supplied to the patient as per the requirement preventing the wastage of oxygen. This is done by constantly monitoring the oxygen level of the patient and releasing the exact amount of oxygen needed by the patient, using a microcontroller device, to control the flow rate of oxygen. This helps automatically to control the oxygen flow and also alert the doctor if the patient goes into a critical condition. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid19 Infection Detection and Classification Using CNN on Chest X-ray Images

Covid-19 has opened up a plethora of worries to the world since the past 2 years. The infection rate and death rate are increasing rapidly. It has worsened by the number of genetic mutations this virus has undergone. Timely detection of the disease is the only way out to handle this health emergency. Severity of this disease is when the virus attacks the major volume of the lung and results in pneumonia. To diagnose the pneumonia the first preferred modality is chest X-ray. There are two solid reasons why the Computer Aided Diagnosis (CAD) system is the need of the hour. First, the volume of X-rays generated for a huge number of infected patients to be assessed and second being the requirement of accuracy in diagnosis. Radiologists find it difficult to assess the severity through bare eyes and most of the time end up making a wrong conclusion which is chaotic decision. With the advent of technology, deep learning algorithms are proving to be most appropriate because of its ability to deliver expected accuracy and capacity to handle huge volume of data. This paper proposed a Deep Learning based Computer Aided Diagnosis System that accepts Chest X-ray image of a patient as input and classifies them as pneumonia or non-pneumonia. The Deep learning model is built and is trained with over 5000 chest X-ray images. Thus, trained model is then tested and validated and an accuracy of 96.66% is achieved. However, since the data is not real time, this work does not claim medical accuracy. The validation plots of the training loss and accuracy and validation loss and accuracy have been validated through regression. © 2021 IEEE.