ABSTRACT
Introduction: in order to control and prevent the spread of COVID-19, people must have adequate knowledge, a positive attitude, and practice basic preventive procedures towards the disease. This study aims to determine the KAP towards COVID-19 among PLHIV undergoing clinical-outpatient follow-up at SSHM. Methods: a hospital-based cross-sectional study was conducted to determine knowledge, attitude and practice (KAP) towards COVID-19 among 344 participants, who were selected using a simple random sampling technique from 4th January to 25th February 2022. A pretested and structured interviewer-administered questionnaire was used for data collection. Results were summarized in frequencies and percentages. The Chi-square test was used to determine factors influencing KAP. Results: among the 344 participants that were enrolled in the study, adequate knowledge and positive attitude scores (≥75 correct answers) towards COVID-19 were reported in 72.4% and 62.5% of the participants, respectively. Most of the respondents (82%) were not practicing basic preventive procedures against COVID-19. Knowledge and attitude scores were significantly associated (p<0.05) with gender, age, marital status and educational status, while practice score was significantly associated (p<0.05) with gender, marital status, educational status, employment status and time since HIV diagnosis. There was a moderate positive correlation (r=0.60) between knowledge and attitude scores, while there was a low positive correlation (r=0.23) between knowledge and practice scores and attitude and practice scores, respectively. Conclusion: people living with HIV have adequate knowledge, a positive attitude and poor practice towards COVID-19. Therefore, in order to mitigate coronavirus infection among People Living with HIV/AIDS (PLHIV), health talks at ART service delivery points should incorporate information on COVID-19 preventative strategies. © Muktar Musa Shallangwa et al.
ABSTRACT
The COVID-19 pandemic had a catastrophic impact on world health and economic. This is attributed to the unavoidable delay in the diagnosis process, due to limitation of COVID-19 test kits. Thus, it is urgently required to establish more cheap and affordable diagnostic approaches. Chest X-ray is an important initial step towards a successful COVID-19 diagnose, where it is easily to detect any chest abnormalities (e.g., lung inflammation). Furthermore, majority of hospitals have X-ray devices that can be used in early COVID-19 diagnosis. However, the shortage of radiologists is a key factor that limits early COVID-19 diagnosis and negatively affects the treatment process. This paper presents an artificial intelligence based technique for early COVID-19 diagnosis from chest X-ray images using medical knowledge and deep Convolutional Neural Networks (CNNs). To this end, a deep learning model is built carefully and fine-tuned to achieve the maximum performance in COVID-19 detection. Experimental results on recent benchmark datasets demonstrate the superior performance of the proposed technique in identifying COVID-19 with 96% accuracy. © 2020 IEEE.