COVID-19 knowledge, attitude, practices, and associated factors among Bangladeshi food handlers from various food industries

Background and objectives It is believed that preventive safety measures are the most effective way to avoid the COVID-19. The adherence of workers to these measures is largely determined by their knowledge, attitude, and practices (KAP). Because they are in close proximity to consumable items, workers in the food industry must be especially vigilant during this period. The purpose of this present study was to evaluate the COVID-19 knowledge, attitudes, and practices of food handlers in different food industries of Bangladesh. Study design This was a cross-sectional study. Methods This included the participation of 400 food handlers from 15 food industries. The information was collected from the participants through a questionnaire prepared in Google form. Different nonparametric tests and a linear regression model were performed for statistical analysis. Results With a correct response rate of about 90% on average (knowledge 89.7%, attitude 93%, practices 88.2%), the participants showed an acceptable KAP (>80% correct response) regarding COVID-19. Education (p = 0.00) and working experiences (p = 0.01) had a significant association with the total KAP scores. Conclusion Food handlers in the food industries of Bangladesh have adequate knowledge, a positive attitude, and the desired practices regarding the COVID-19 issue. Graphical Image 1

COVID-19 knowledge, attitude, practices, and associated factors among Bangladeshi food handlers from various food industries.

Background and objectives: It is believed that preventive safety measures are the most effective way to avoid the COVID-19. The adherence of workers to these measures is largely determined by their knowledge, attitude, and practices (KAP). Because they are in close proximity to consumable items, workers in the food industry must be especially vigilant during this period. The purpose of this present study was to evaluate the COVID-19 knowledge, attitudes, and practices of food handlers in different food industries of Bangladesh. Study design: This was a cross-sectional study. Methods: This included the participation of 400 food handlers from 15 food industries. The information was collected from the participants through a questionnaire prepared in Google form. Different nonparametric tests and a linear regression model were performed for statistical analysis. Results: With a correct response rate of about 90% on average (knowledge 89.7%, attitude 93%, practices 88.2%), the participants showed an acceptable KAP (>80% correct response) regarding COVID-19. Education (p = 0.00) and working experiences (p = 0.01) had a significant association with the total KAP scores. Conclusion: Food handlers in the food industries of Bangladesh have adequate knowledge, a positive attitude, and the desired practices regarding the COVID-19 issue.

COVID-19 Cases Detection from Chest X-Ray Images using CNN based Deep Learning Model

COVID-19 has recently manifested as one of the most serious life-threatening infections and is still circulating globally. COVID-19 can be contained to a considerable extent if a patient can know their COVID-19 infection at a possible earlier time, and they can be isolated from other individuals. Recently, researchers have explored AI (Artificial Intelligence) based technologies like deep learning and machine learning strategies to identify COVID-19 infection. Individuals can detect COVID-19 disease using their phones or computers, dispensing with the need for clinical specimens or visits to a diagnostic center. This can significantly reduce the risk of spreading COVID-19 farther from a probably infected patient. Motivated by the above, we propose a deep-learning model using CNN (Convolutional Neural Networks) to autonomously diagnose COVID-19 disease from CXR (Chest X-ray) images. The dataset used to train our model includes 10293 X-ray images, with 875 X-ray images from COVID-19 cases. The dataset contains three different classes of the tuple: COVID-19, pneumonia, and normal cases. The empirical outcomes show that the proposed model achieved 97%specificity, 96.3% accuracy, 96% precision, 96% sensitivity, and 96% F1-score, respectively, which are better than the available works, despite using a CNN with fewer layers than those.