The objective of this research is to develop a convolutional neural network model 'COVID-Screen-Net' for multi-class classification of chest X-ray images into three classes viz. COVID-19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X-ray images and accurately identifies the features responsible for distinguishing the X-ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine-tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X-ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the 'COVID-Screen-Net' outperforms the existing systems for screening of COVID-19. The effectiveness of the model is validated by the radiology experts on the real-time dataset. Therefore, it may prove a useful tool for quick and low-cost mass screening of patients of COVID-19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number 'SW-13625/2020'.In 2020 the world is facing unprecedented challenges due to COVID-19. To address these challenges, many digital tools are being explored and developed to contain the spread of the disease. With the lack of availability of vaccines, there is an urgent need to avert resurgence of infections by putting some measures, such as contact tracing, in place. While digital tools, such as phone applications are advantageous, they also pose challenges and have limitations (eg, wireless coverage could be an issue in some cases). On the other hand, wearable devices, when coupled with the Internet of Things (IoT), are expected to influence lifestyle and healthcare directly, and they may be useful for health monitoring during the global pandemic and beyond. In this work, we conduct a literature review of contact tracing methods and applications. Based on the literature review, we found limitations in gathering health data, such as insufficient network coverage. To address these shortcomings, we propose a novel intelligent tool that will be useful for contact tracing and prediction of COVID-19 clusters. The solution comprises a phone application combined with a wearable device, infused with unique intelligent IoT features (complex data analysis and intelligent data visualization) embedded within the system to aid in COVID-19 analysis. Contact tracing applications must establish data collection and data interpretation. Intelligent data interpretation can assist epidemiological scientists in anticipating clusters, and can enable them to take necessary action in improving public health management. Our proposed tool could also be used to curb disease incidence in future global health crises.COVID-19 was first reported as an unknown group of pneumonia in Wuhan City, Hubei province of China in late December of 2019. The rapid increase in the number of cases diagnosed with COVID-19 and the lack of experienced radiologists can cause diagnostic errors in the interpretation of the images along with the exceptional workload occurring in this process. Therefore, the urgent development of automated diagnostic systems that can scan radiological images quickly and accurately is important in combating the pandemic. With this motivation, a deep convolutional neural network (CNN)-based model that can automatically detect patterns related to lesions caused by COVID-19 from chest computed tomography (CT) images is proposed in this study. In this context, the image ground-truth regarding the COVID-19 lesions scanned by the radiologist was evaluated as the main criteria of the segmentation process. A total of 16 040 CT image segments were obtained by applying segmentation to the raw 102 CT images. Then, 10 420 CT image segments related to healthy lung regions were labeled as COVID-negative, and 5620 CT image segments, in which the findings related to the lesions were detected in various forms, were labeled as COVID-positive. With the proposed CNN architecture, 93.26% diagnostic accuracy performance was achieved. The sensitivity and specificity performance metrics for the proposed automatic diagnosis model were 93.27% and 93.24%, respectively. Additionally, it has been shown that by scanning the small regions of the lungs, COVID-19 pneumonia can be localized automatically with high resolution and the lesion densities can be successfully evaluated quantitatively.Finding messaging to promote the use of face masks is fundamental during a pandemic. Study 1 (N = 399) shows that telling people to "rely on their reasoning" increases intentions to wear a face mask, compared with telling them to "rely on their emotions." In Study 2 (N = 591) we add a baseline. https://www.selleckchem.com/products/ro5126766-ch5126766.html However, the results show only a non-significant trend. Study 3 reports a well-powered replication of Study 2 (N = 930). In line with Study 1, this study shows that telling people to "rely on their reasoning" increases intentions to wear a face mask, compared to telling them to "rely on their emotions." Two internal meta-analyses show that telling people to "rely on their reasoning" increases intentions to wear a face mask compared (1) to telling them to "rely on their emotions" and (2) to the baseline. These findings suggest interventions to promote intentions to wear a face mask.Societal crises and stressful events are associated with an upsurge of conspiracy beliefs that may help people to tackle feelings of lack of control. In our study (N = 783), we examined whether people with higher feelings of anxiety and lack of control early in the COVID-19 pandemic endorse more conspiracy theories. Our results show that a higher perception of risk of COVID-19 and lower trust in institutions' response to the pandemic were related to feelings of anxiety and lack of control. Feeling the lack of control, but not anxiety, independently predicted COVID-19 conspiracy theory endorsement. Importantly, COVID-19 conspiracy beliefs were strongly correlated with generic conspiracy and pseudoscientific beliefs, which were likewise associated with the feeling of lack of control and lower trust in institutions. The results highlight that considering people's emotional responses to the COVID-19 pandemic is crucial for our understanding of the spread of conspiracy and pseudoscientific beliefs.