ABSTRACT
BACKGROUND: SARS-CoV-2 can spread rapidly on maritime platforms. Several outbreaks of SARS-CoV-2 have been reported on warships at sea, where transmission is facilitated by living and working in close quarters. Core components of infection control measures such as social distancing, patient isolation and quarantine of exposed persons are extremely difficult to implement. Whole genome sequencing (WGS) of SARS-CoV-2 has facilitated epidemiological investigations of outbreaks, impacting on outbreak management in real time by identifying transmission patterns, clusters of infection and guiding control measures. We suggest such a capability could mitigate against the impact of SARS-CoV-2 in maritime settings. METHODS: We set out to establish SARS-CoV-2 WGS using miniaturised nanopore sequencing technology aboard the Royal Fleet Auxiliary ARGUS while at sea. Objectives included designing a simplified protocol requiring minimal reagents and processing steps, the use of miniaturised equipment compatible for use in limited space, and a streamlined and standalone data analysis capability to allow rapid in situ data acquisition and interpretation. RESULTS: Eleven clinical samples with blinded SARS-CoV-2 status were tested at sea. Following viral RNA extraction and ARTIC sequencing library preparation, reverse transcription and ARTIC PCR-tiling were performed. Samples were subsequently barcoded and sequenced using the Oxford Nanopore MinION Mk1B. An offline version of the MinKNOW software was used followed by CLC Genomics Workbench for downstream analysis for variant identification and phylogenetic tree construction. All samples were correctly classified, and relatedness identified. CONCLUSIONS: It is feasible to establish a small footprint sequencing capability to conduct SARS-CoV-2 WGS in a military maritime environment at sea with limited access to reach-back support. This proof-of-concept study has highlighted the potential of deploying such technology in the future to military environments, both maritime and land-based, to provide meaningful clinical data to aid outbreak investigations.
ABSTRACT
This paper proposes transferred initialization with modified fully connected layers for COVID-19 diagnosis. Convolutional neural networks (CNN) achieved a remarkable result in image classification. However, training a high-performing model is a very complicated and time-consuming process because of the complexity of image recognition applications. On the other hand, transfer learning is a relatively new learning method that has been employed in many sectors to achieve good performance with fewer computations. In this research, the PyTorch pre-trained models (VGG19_bn and WideResNet -101) are applied in the MNIST dataset for the first time as initialization and with modified fully connected layers. The employed PyTorch pre-trained models were previously trained in ImageNet. The proposed model is developed and verified in the Kaggle notebook, and it reached the outstanding accuracy of 99.77% without taking a huge computational time during the training process of the network. We also applied the same methodology to the SIIM-FISABIO-RSNA COVID-19 Detection dataset and achieved 80.01% accuracy. In contrast, the previous methods need a huge compactional time during the training process to reach a high-performing model. Codes are available at the following link: github.com/dipuk0506/Spina1Net © 2022 IEEE.
ABSTRACT
Over the past year, COVID-19 has become a global pandemic and people across the globe have suffered a lot from this pandemic. The rate of transmitting the coronavirus in people is very quick. A rapid diagnosis can potentially help governments in identifying the pattern of transmission. There are some tests available but those tests take a long time to give the report. So, in this work, we have proposed a model that will distinguish between normal people, COVID affected people, and pneumonia affected people with the help of an X-ray. X-ray images are considered because taking an X-ray image is very little time-consuming. In this work, we have trained the X-ray images with a novel Deep Learning approach with SpinalNet architecture, and that distinguishes normal people, COVID affected people, and pneumonia affected people. After training the model we have achieved a very good accuracy for the SpinalNet architecture that is 96.12% while the traditional model provides 95.50% accuracy. We present precision, recall, and Fl scores of COVID and Pneumonia classes. We also present our results and codes with execution details. This paper contains the link to Kaggle notebooks with execution details. The applied Spinalnet transfer learning code is available in our GitHub repository: https://github.com/dipuk0506/SpinalNet
ABSTRACT
Dhaka, capital of COVID-19 s 9th most infected country a day, is appallingly receiving 9, 422.1 infected per million to date. To upset this intimidating infected rate, public awareness, and adherence to control measures is a dire need. The objective of this cross-sectional analysis is to gauge the awareness and disclose the associating factors that influence the awareness among the adult women of Dhaka city, based on KAP theory. A self-administrated questionnaire following the national guidelines was developed to assess knowledge, attitudes, and practices of COVID-19 among respondents, which was disseminated to 139 respondents deploying the quota sampling process. Statistical representation of the baseline data spotted that only 56.11% of the participants own fair knowledge, while 83.44% demonstrates optimistic attitudes towards COVID-19. However, merely 52% of the respondents reported following the precautionary measures against COVID-19. Cross-tabulation on the surveyed data disclose that education, occupation, and monthly family income are significant predictors (p<0.05) of knowledge among the respondents. Significant Odd Ratios (p<0.05) further justifies the reflections of respondents' knowledge on the attitudinal and behavioral statements. This study detected suboptimal awareness and urged for the collective efforts orchestrated by the Ministry of Health to intensify the awareness through recommended channel to reduce associated health risk. © 2020 IEEE.