RESUMO
Bangladesh is located in Southeast Asia that has a high population density. It is a lower-middle-income country. The COVID-19 pandemic severely impacted the nation that slowed its economic growth. It halted major industries, crippling the nation's economy. The students were uncertain after the declaration of school closures. Hospitals could not treat other patients properly due to the vast health burden of COVID-19. Bangladesh put up a solid fight during COVID-19 as a lower-middle-income country. Prompt action, early vaccination drives, effective awareness campaigns, and widespread public involvement have enabled Bangladesh to bring more than 90% of its population into COVID-19 vaccination coverage. It was possible by the effective diplomatic and local health strategy implemented by the Bangladeshi government, the country's extensive prior experience, and its long history of achieving a high success rate in other vaccination campaigns. Bangladesh was able to flatten the curve sooner than other developed countries. Therefore, the cogs of everyday social life and the economy begin to turn once more. The strategy Bangladesh used to combat the COVID-19 pandemic through vaccination and diplomatic policy by calling on its prior experience has the potential to serve as a model for other low- and middle-income countries and an example for developed nations.
RESUMO
The discovery of the SARS-CoV-2 Omicron (B.1.1.529) variant has sparked alarm globally because of its rapid rate of infection and trespassing acquired immunity due to vaccination or natural infection. This heavily mutated variant is rapidly spreading around the world. Infected individuals with the Omicron variant may suffer from flu-like symptoms, and infected with the Delta variant frequently report low oxygen levels, high pulse rates, and a loss of smell and taste. Also, the Omicron variant causes asymptomatic or mild disease so far, and not any severe illness as like Delta, and this new variant has a 15% to 80% reduced risk of hospitalization than the Delta variant. Scientists are worried about the possibility of escaping the immunity by the Omicron variants and subvariants among fully vaccinated and recovered COVID-19 patients. Two doses of available vaccines are found to be partially ineffective in protecting this new variant, therefore, the third dose as a booster is recommended to enhance antibody level. Moreover, some antiviral drugs significantly reduce hospitalization or death among mild to severe COVID-19 patients. All authorized antiviral drugs are effective against viral replication for most SARS-CoV-2 variants, and particularly some monoclonal antibodies may not now be effective in treating COVID-19 patients. There is an urgent need to update existing vaccines, develop more effective and newer vaccines as well as additional monoclonal antibodies to counter Omicron. Therefore, along with close monitoring of Omicron characteristics, the present study suggests that health safety guidelines, mass immunization, early diagnosis, and search for effective antiviral drugs should be the approaches to fight against newer SARS-CoV-2 variants.
RESUMO
OBJECTIVE: Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. APPROACH: A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. MAIN RESULTS: A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min-1 across 10 subjects. The maximum individual ITR is 107.55 bits min-1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min-1 are achieved with CCA and NCCA respectively. SIGNIFICANCE: The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.