ABSTRACT
The COVID-19 vaccine has been made available for emergency use in Bangladesh. However, willingness to receive the vaccine may be affected by varying factors across the country. Therefore, this study aimed to investigate the factors that influence willingness to receive the vaccine among Bangladeshi adults. A population-based cross-sectional online survey was conducted among a sample of 1,725 Bangladesh adults (age 18 years and older). The statistical analysis included univariate, bivariate and multivariate regression model. Findings show that 85% (n = 1463) of respondents were willing to receive the vaccine. Respondents with 1–2 children (aOR: 1.77, 95% CI: 1.00–3.13, P =. 048), perceived risk of being infected (aOR: 1.48, 95% CI: 1.03–2.14, P =. 03), perceived impact on daily life (aOR: 2.53, 95%CI: 1.45–4.44, P =. 001), history of co-morbidities (aOR: 2.04, 95% CI: 1.37–3.04, P <. 01), price of the vaccine (aOR: 3.58, 95% CI: 2.34–5.47), physician’s recommendation to receive vaccine (aOR: 2.06, 95% CI: 1.38–3.06, P <. 01), vaccines supplied by government (aOR: 2.31, 95% CI: 1.64–3.25, P <. 01) were found to be motivating factors for willingness to receive the vaccine. Findings indicate that willingness to receive the vaccine is likely to be affected by socio-demographic, and health system factors. This should be carefully considered in the rollout of the vaccination plans in Bangladesh. © 2021 SAGE Publications.
ABSTRACT
The novel coronavirus (COVID-19), a highly infectious disease that first found at Wuhan Province of China in Dec 2019, spread worldwide in some months and already become a pandemic. Covid-19 has already changed the world economic structure, people's religious, political, social life, public health structure, people's daily life structure and also made millions of people jobless. The only way to fight this epidemic is to identify the infected person as soon as possible and separate them from a healthy person, so that they can't infect anyone again. At present, RT-PCR is currently used to detect coronavirus patients around the world. But the World Health Organization (WHO) said that RT-PCR suffers from low sensitivity and low specificity for early-stage cases. Recent research has shown that chest CT scan images play a beneficial role in identifying coronavirus cases. In this study, we compared the performances of four classification algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Extra Trees (ET), and Convolutional Neural Network (CNN) for classifying COVID-19 cases and proposed a prediction model based on classification results. The result shows that our proposed CNN model outperformed the other classification algorithms and obtained an accuracy of 98.0%. © 2021 IEEE.