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Guided by the behavioral model of health service use, this study examined the effect of South Asians' perceptions of healthcare, religious belief, and socioeconomic status on their perceived benefits and risks of COVID-19 vaccines (N = 245). Cross-sectional survey was used. Logistic regressions results showed that higher levels of perceived involvement in South Asian community health and trust in the healthcare system were associated with higher odds of reporting perceived vaccine benefits. Permanent residents, students (vs. unemployed), and Pakistani (vs. Indians) also perceived the vaccine as beneficial. On the other hand, believing that the body was sacred and being Buddhist (vs. Hindu) were associated with higher odds of perceiving severe vaccination risk. Those who believed that God would cure COVID-19 and those with higher education tended to perceive the vaccine as having a limited effect. Implications on designing culturally appropriate COVID-19 vaccines messages in interethnic settings are discussed., (© 2022 Wiley Periodicals LLC.)

The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps.

Background: The COVID-19 pandemic continues to have a disproportionate effect on ethnic minorities. Across countries, greater vaccine hesitancy has been observed among ethnic minorities. After excluding foreign domestic helpers, South Asians make up the largest proportion of ethnic minorities in Hong Kong. It is necessary to plan for COVID-19 vaccination promotional strategies that cater to the unique needs of South Asians in Hong Kong., Objective: This study investigated the prevalence of COVID-19 vaccine uptake among a sample of South Asians in Hong Kong. We examined the effects of sociodemographic data and factors at individual level (perceptions), interpersonal level (information exposure on social media), and sociostructural level (cultural) based on the socioecological model., Methods: A cross-sectional web-based survey was conducted on May 1-31, 2021. Participants were South Asian people aged 18 years or older living in Hong Kong; able to comprehend English, Hindi, Nepali, or Urdu; and having access to a smartphone. Three community-based organizations providing services to South Asians in Hong Kong facilitated the data collection. The staff of the community-based organizations posted the study information in WhatsApp groups involving South Asian clients and invited them to participate in a web-based survey. Logistic regression models were fit for data analysis., Results: Among 245 participants, 81 (33.1%) had taken at least one dose of the COVID-19 vaccine (one dose, 62/245, 25.2%; and both doses, 19/245, 7.9%). After adjusting for significant background characteristics, cultural and religious reasons for COVID-19 vaccine hesitancy were associated with lower COVID-19 vaccine uptake (adjusted odds ratio [AOR] 0.83, 95% CI 0.71-0.97; P=.02). At the individual level, having more positive attitudes toward COVID-19 vaccination (AOR 1.31, 95% CI 1.10-1.55; P=.002), perceived support from significant others (AOR 1.29, 95% CI 1.03-1.60; P=.03), and perceived higher behavioral control to receive COVID-19 vaccination (AOR 2.63, 95% CI 1.65-4.19; P<.001) were associated with higher COVID-19 vaccine uptake, while a negative association was found between negative attitudes and the dependent variable (AOR 0.73, 95% CI 0.62-0.85; P<.001). Knowing more peers who had taken the COVID-19 vaccine was also associated with higher uptake (AOR 1.39, 95% CI 1.11-1.74; P=.01). At the interpersonal level, higher exposure to information about deaths and other serious conditions caused by COVID-19 vaccination was associated with lower uptake (AOR 0.54, 95% CI 0.33-0.86; P=.01)., Conclusions: In this study, one-third (81/245) of our participants received at least one dose of the COVID-19 vaccine. Cultural or religious reasons, perceptions, information exposure on social media, and influence of peers were found to be the determinants of COVID-19 vaccine uptake among South Asians. Future programs should engage community groups, champions, and faith leaders, and develop culturally competent interventions., (©Akansha Singh, Angel Hor Yan Lai, Jingxuan Wang, Saba Asim, Paul Shing-Fong Chan, Zixin Wang, Eng Kiong Yeoh. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 09.11.2021.)

Guided by the behavioral model of health service use, this study examined the effect of South Asians' perceptions of healthcare, religious belief, and socioeconomic status on their perceived benefits and risks of COVID‐19 vaccines (N = 245). Cross‐sectional survey was used. Logistic regressions results showed that higher levels of perceived involvement in South Asian community health and trust in the healthcare system were associated with higher odds of reporting perceived vaccine benefits. Permanent residents, students (vs. unemployed), and Pakistani (vs. Indians) also perceived the vaccine as beneficial. On the other hand, believing that the body was sacred and being Buddhist (vs. Hindu) were associated with higher odds of perceiving severe vaccination risk. Those who believed that God would cure COVID‐19 and those with higher education tended to perceive the vaccine as having a limited effect. Implications on designing culturally appropriate COVID‐19 vaccines messages in interethnic settings are discussed. [ABSTRACT FROM AUTHOR]

In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan. [ABSTRACT FROM AUTHOR]

Background: Few studies have explored the determinants of health-related quality of life (HRQoL) in the elderly during the COVID-19 pandemic. Identifying these factors may help implement appropriate policies to enhance HRQoL in the elderly. Therefore, we aimed to identify the predictors of physical and mental component summary (PCS and MCS) scores of HRQoL in selected six low- and middle-income Asian countries. Methods: We conducted an online survey of older people aged ≥55 years in six countries: Bangladesh, Iran, Iraq, Malaysia, Palestine, and Sri Lanka. The Stark QoL questionnaire was used to measure the PCS and MCS scores. Univariate and multiple variable analyses after adjusting for confounders were performed to identify the possible predictors of PCS and MCS. Results: A total of 1644 older people (69.1 ± 7.8 years, range 55–97 years, Female: 50.9%) responded to the survey. We documented age, country of residence, marital status, number of male children, current employment status, and health insurance, ability to pay household bills, frequency of family members visits and receiving support during COVID-19 pandemic predicted both PCS and MCS. However, gender, residence, and number of female children were associated with PCS only (all p < 0.05). Conclusion: Socio-demographic factors such as age, country of residence, marital status, number of male children, current employment status, health insurance, ability to pay household bills, frequency of family members visiting family members, and receiving support during the COVID-19 pandemic affecting both physical and mental quality of life. These results can guide formulating health care planning policies to enhance QoL during COVID-19 and future pandemics in the elderly. [ABSTRACT FROM AUTHOR]