While people around the world are terrified of the global pandemic coronavirus disease 2019 (COVID-19) and no registered vaccine is out yet, undertaking preventive safety measures are said to be the only way to stay away from it. People’s adherence to these measures is broadly dependent on their knowledge, attitude, and practices (KAP). The present study was designed to evaluate workers’ knowledge, attitude, and practices from a food industry in Dhaka, Bangladesh, towards COVID-19. A number of 155 respondents took part in this online-based study. The information was acquired online from the participants through a questionnaire prepared in Google form. With a correct response rate of about 90% on average (knowledge 89.7%, attitude 93%, practices 88.2%), the participants showed a good level of KAP regarding COVID-19. However, education and working experiences had a significant association with the total KAP scores (p < 0.05). Further KAP studies in different generic food industries in Bangladesh should be carried out to bring a more precise picture for ensuring the level of workplace and worker’s safety.

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The Rohingya refugee population in Bangladesh has become more vulnerable to COVID-19 because of their living and environmental conditions. The current study represents an assessment of the Rohingya people's COVID-19-related knowledge, attitude, and practices (KAP) at eight refugee camps in Cox's Bazar. This cross-sectional study was completed with a total of 400 responses between July and September of 2020. A questionnaire was created to assess demographic characteristics (5 items), knowledge (10 items), attitude (5 items), practices (5 items), and information sources (1 item). Aside from the total KAP scores, the scores are also presented based on demographic variables. The KAP of the respondents were not satisfactory, with scores of 5.8 (1.8), 2.2 (1.0), and 0.9 (0.7), respectively. We found significant differences only in the knowledge scores based on education and gender. In conclusion, this study emphasizes the importance of COVID-19 training that focuses on behavioral changes for the Rohingya people in Bangladesh.

In a general COVID-19 population in Cox’s Bazar, Bangladesh, we developed a medication recommendation system based on clinical information from the electronic medical record (EMR). Our goal was also to enable deep learning (DL) strategies to quickly assist physicians and COVID-19 patients by recommending necessary medications. The general demographic data, clinical symptoms, basic clinical tests, and drug information of 8953 patients were used to create a dataset. The learning model in this COVID-MED model was created using Keras (an open-source artificial neural network library) to solve regression problems. In this study, a sequential model was adopted. In order to improve the prediction capability and achieve global minima quickly and smoothly, the COVID-MED model incorporates an adaptive optimizer dubbed Adam. The model calculated a mean absolute error of 0.0037, a mean squared error of 0.000035, and a root mean squared error of 0.0059. The model predicts the output medications, such as injections or other oral medications, with around 99% accuracy. These findings show that medication can be predicted using information from the EMR. Similar models allow for patient-specific decision support to help prevent medication errors in diseases other than COVID-19.