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2.
Microbiology Spectrum ; : e0199822, 2022.
Article in English | MEDLINE | ID: covidwho-2137461

ABSTRACT

Accurate and early diagnoses are prerequisites for prompt treatment. For coronavirus disease 2019 (COVID-19), it is even more crucial. Currently, choice of methods include rapid diagnostic tests and reverse transcription polymerase chain reaction (RT-PCR) using samples mostly of respiratory origin and sometimes saliva. We evaluated two rapid diagnostic tests with three specimen types using viral transport medium (VTM) containing naso-oropharyngeal (NOP) swabs, direct nasal and direct nasopharyngeal (NP) samples from 428 prospective patients. We also performed RT-PCR for 428 NOP VTM and 316 saliva samples to compare results. The sensitivity of the SD Biosensor Standard Q COVID-19 antigen (Ag) test kit drastically raised from an average of 65.55% (NOP VTM) to 85.25% (direct nasal samples), while RT-PCR was the gold standard. For the CareStart kit, the sensitivity was almost similar for direct NP swabs;the average was 84.57%. The specificities were >=95% for both SD Biosensor Standard Q and CareStart COVID-19 Ag tests in all platforms. The kits were also able to detect patients with different variants as well. Alternatively, RT-PCR results from saliva and NOP VTM samples showed high sensitivities of 96.45% and 95.48% with respect to each other as standard. The overall results demonstrated high performance of the rapid tests, indicating the suitability for regular surveillance at clinical facilities when using direct nasal or direct NP samples rather than NOP VTM. Additionally, the analysis also signifies not showed that RT-PCR of saliva can be used as an choice of method to RT-PCR of NOP VTM, providing an easier, non-invasive sample collection method. IMPORTANCE There are several methods for the diagnosis of coronavirus disease 2019 (COVID-19), and the choice of methods depends mostly on the resources and level of sensitivity required by the user and health care providers. Still, reverse transcription polymerase chain reaction (RT-PCR) has been chosen as the best method using direct naso-oropharyngeal swabs. There are also other methods of fast detection, such as rapid diagnostic tests (RDTs), which offer result within 15 to 20 min and have become quite popular for self-testing and in the clinical setting. The major drawback of the currently used RT-PCR method is compliance, as it may cause irritation, and patients often refuse to test in such a way. RDTs, although inexpensive, suffer from low sensitivity due to technical issues. In this article, we propose saliva as a noninvasive source for RT-PCR samples and evaluate various specimen types at different times after infection for the best possible output from COVID-19 rapid tests.

3.
Journal of System and Management Sciences ; 12(5):1-20, 2022.
Article in English | Scopus | ID: covidwho-2120633

ABSTRACT

Machine Learning methods have been used to combat COVID-19 since the pandemic has started in year 2020. In this regard, most studies have focused on detecting and identifying the characteristics of SARS-CoV-2, especially via image processing. Some studies have applied machine learning for contact tracing to minimise the transmission of COVID-19 cases. Limited work has, however, reported on how geospatial features have an influence on the transmission of COVID-19 and formation of clusters at local scale. Therefore, this paper has aimed to study the importance of geospatial features that had resorted to COVID-19 cluster formation in Kuala Lumpur, Malaysia in year 2021. Several datasets were used in this work, which have included the address details of confirmed positive COVID-19 cases and the details of nearby residential areas and Points of Interest (POI) located within the federal territory of Kuala Lumpur. The datasets were pre-processed and transformed into an analytical dataset for conducting empirical investigations. Various feature selection methods were applied, including the Boruta Algorithm, Chi-square (Chi2) Test, Extra Trees Classifier (ETC), Recursive Feature Elimination (RFE) method, and Deep Learning Autoencoder (DLA). Detailed investigations on the top-n features were performed to elicit a set of optimal features. Subsequently, several machine learning models were trained using the optimal features, including Logistic Regression (LR), Random Forest Classifier (RFC), Naïve Bayes Classifier (NBC), and Extreme Gradient Boosting (XGBoost). It was revealed that Boruta produced the optimal number of features with n = 96, whereas RFC achieved the best prediction results compared to other classifiers, with around 95% accuracy. Consequently, the findings in this paper help to recognize the geospatial features that have impacts on the formation of COVID-19 and other infectious disease clusters at local scale. © 2022, Success Culture Press. All rights reserved.

4.
IEEE Region 10 Symposium (TENSYMP) - Good Technologies for Creating Future ; 2021.
Article in English | Web of Science | ID: covidwho-1853492

ABSTRACT

Particulate matters having diameters of 2.5 micrometers or less (PM2.5) have been linked with life threatening health issues worldwide. Data centric approach to ascertain the patterns in the propagation of PM2.5 materials in the atmosphere of a region can help policy makers take informed decisions to take proper action. In this paper, we analyze and identify seasonal, hourly, and regional patterns of PM2.5 propagation in Bangladesh from 2017 to 2020 using the Berkeley Earth dataset. We observe that the concentration of PM2.5 particles has a nationwide median value of about 50 mu gm(-3), which is unhealthy for sensitive individuals. The concentration varies seasonally and diurnally. We observe that the concentrations of PM2.5 in the air is around five times more in winter than in summer. The mean PM2.5 concentration inside Dhaka is significantly worse around 70 mu gm(-3), which is 1.25 times than the average concentration throughout Bangladesh. We also observe average concentration dropped during the covid-19 pandemic due to lockdown. Using cross correlation analysis, we observed how spikes in PM2.5 concentration levels in one zone may correspond with peaked concentrations in a different zone a few hours later, indicating that air currents may cause the particles to move in certain directions. Our exploratory analysis serves as the first cross-country data centric study of the state and propagation patterns of PM2.5 particles within Bangladesh and our findings can serve as foundation for further research on the topic.

5.
2021 IEEE Region 10 Symposium, TENSYMP 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1494347

ABSTRACT

Particulate matters having diameters of 2.5 micrometers or less (PM2.5) have been linked with life threatening health issues worldwide. Data centric approach to ascertain the patterns in the propagation of PM2.5 materials in the atmosphere of a region can help policy makers take informed decisions to take proper action. In this paper, we analyze and identify seasonal, hourly, and regional patterns of PM2.5 propagation in Bangladesh from 2017 to 2020 using the Berkeley Earth dataset. We observe that the concentration of PM2.5 particles has a nationwide median value of about 50 μgm-3, which is unhealthy for sensitive individuals. The concentration varies seasonally and diurnally. We observe that the concentrations of PM2.5 in the air is around five times more in winter than in summer. The mean PM2.5 concentration inside Dhaka is significantly worse around 70 μgm-3, which is 1.25 times than the average concentration throughout Bangladesh. We also observe average concentration dropped during the covid-19 pandemic due to lockdown. Using cross correlation analysis, we observed how spikes in PM2.5 concentration levels in one zone may correspond with peaked concentrations in a different zone a few hours later, indicating that air currents may cause the particles to move in certain directions. Our exploratory analysis serves as the first cross-country data centric study of the state and propagation patterns of PM2.5 particles within Bangladesh and our findings can serve as foundation for further research on the topic. © 2021 IEEE.

6.
Consultant ; 60(11):3-13, 2020.
Article in English | EMBASE | ID: covidwho-1370004

ABSTRACT

Multiple chronic medical conditions are common to patients served by the community mental health (CMH) system. Medical diseases are present in at least 50% of all patients with psychiatric conditions, and severe mental disorders are associated with significant physical comorbidity and mortality. Early data show that individuals with preexisting multiple chronic conditions have a higher mortality risk when they are symptomatic with COVID-19. Although mitigation guidelines and recommendations are constantly being reviewed and updated, we found no specific recommendations targeting the vulnerable population who use CMH systems or the publicly funded and managed behavioral health entities which serve them. We reviewed the Centers for Disease Control and Prevention guidelines regarding infection control in health care facilities that provide ambulatory care, including behavioral health clinics, as well as reviewed recent population outcomes data. We posit that the population served by the CMH systems is a higher-risk cohort than the general population and offer recommendations for effective infection prevention strategies specific to this population.

7.
Economic and Political Weekly ; 55(34):28-33, 2020.
Article in English | Scopus | ID: covidwho-891832

ABSTRACT

The way Covid-19 pandemic is being understood and addressed makes it a spectacle in the Debordian sense, in which all the social relations are mediated through images and appearances. Where even the desire of a safe and healthy life is dealt not with dignity, and effective, accessible healthcare, but through virtual images. Such a spectacle, in turn, creates a world that would be connected more, while the people would live a fragmented life on which they will increasingly lose their control. © 2020 Economic and Political Weekly. All rights reserved.

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