Detection of SARS-CoV-2 RNA by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) on Self-Collected Nasal Swab Compared With Professionally Collected Nasopharyngeal Swab

Background: Self-collection of nasal swabs for the detection of SARS-CoV-2 RNA by reverse transcription-polymerase chain reaction (RT-PCR) would considerably increase the testing capability and decrease the risk of transmission among healthcare workers (HCW) and the use of personal protective equipment (PPE). Objectives: This study aimed to evaluate the performance of self-collected nasal swabs compared with professionally collected nasopharyngeal (NP) swabs for detection of SARS-CoV-2 RNA by RT-PCR. Materials and methods: We performed a cross-sectional study where the suspected cases of coronavirus disease 2019 (COVID-19) were instructed about the self-collection of nasal swabs from their mid-turbinate. The results were compared to a nasopharyngeal swab collected by a trained healthcare worker in the same patient at the same sitting. Results: We enrolled 100 participants, of which, 69 (69%) were male and 31 (31%) were female. The median age of the study participant was 36 years. Of the participants, 58 (58%) were symptomatic, and the commonest clinical presentation was cough, which was present in 42 (42%) participants. Out of 100 samples, 31 (31%) professionally collected nasopharyngeal swabs and 28 (28%) self-collected nasal swabs were positive for SARS-CoV-2 by RT-PCR. Out of 31 professionally collected positive samples, three samples were negative in self-collection. Out of 28 self-collected positive samples, no sample was negative in the professional collection. The sensitivity and specificity of self-collected nasal swabs compared to professionally collected nasopharyngeal swabs were 90.32% and 100.00%, respectively. The sensitivity of self-collected nasal was 100% when the cycle threshold (Ct) value of the professionally collected NP swab was less than 30. Conclusion: Our study showed that self-collected nasal swabs' sensitivities were similar to professionally collected NP swabs with a high viral load (low Ct value). Hence, this method could be used when the patient is symptomatic and come to the health providers in the early stage of COVID-19 illness.

Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis.

The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.

Prediction of Epidemics Trend of COVID-19 in Bangladesh.

Background: Amid a critical and emergent situation like the coronavirus disease (COVID-19) pandemic related to extreme health and economic repercussions, we used and presented the mathematical modeling like susceptible-infectious-recovered (SIR) to have a numerical demonstration that can shed light to decide the fate of the scourge in Bangladesh. To describe the idea about the factors influencing the outbreak data, we presented the current situation of the COVID-19 outbreak with graphical trends. Methods: Primary data were collected and analyzed by using a pre-created Google Survey form having a pre-set questionnaire on the social distancing status of different districts. Secondary data on the total and the daily number of laboratory tests, confirmed positive cases, and death cases were extracted from the publicly available sources to make predictions. We estimated the basic reproduction number (R◦) based on the SIR mathematical model and predicted the probable fate of this pandemic in Bangladesh. Results: Quarantine situations in different regions of Bangladesh were evaluated and presented. We also provided tentative forecasts until 31 May 2020 and found that the predicted curve followed the actual curve approximately. Estimated R◦-values (6.924) indicated that infection rate would be greater than the recovery rate. Furthermore, by calibrating the parameters of the SIR model to fit the reported data, we assume the ultimate ending of the pandemic in Bangladesh by December 2022. Conclusion: We hope that the results of our analysis could contribute to the elucidation of critical aspects of this outbreak and help the concerned authority toward decision making.