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Abstract

This study in rural Punjab confirms that findings of a previous study in Chitral, northern Pakistan, of high levels of emotional distress and psychiatric morbidity among women in rural areas of Pakistan.

Background and objectives The pandemic of novel coronavirus disease 2019 (COVID-19) has severely impacted human society with a massive death toll worldwide. There is an urgent need for early and reliable screening of COVID-19 patients to provide better and timely patient care and to combat the spread of the disease. In this context, recent studies have reported some key advantages of using routine blood tests for initial screening of COVID-19 patients. In this article, first we present a review of the emerging techniques for COVID-19 diagnosis using routine laboratory and/or clinical data. Then, we propose ERLX which is an ensemble learning model for COVID-19 diagnosis from routine blood tests. Method The proposed model uses three well-known diverse classifiers, extra trees, random forest and logistic regression, which have different architectures and learning characteristics at the first level, and then combines their predictions by using a second level extreme gradient boosting (XGBoost) classifier to achieve a better performance. For data preparation, the proposed methodology employs a KNNImputer algorithm to handle null values in the dataset, isolation forest (iForest) to remove outlier data, and a synthetic minority oversampling technique (SMOTE) to balance data distribution. For model interpretability, features importance are reported by using the SHapley Additive exPlanations (SHAP) technique. Results The proposed model was trained and evaluated by using a publicly available data set from Albert Einstein Hospital in Brazil, which consisted of 5,644 data samples with 559 confirmed COVID-19 cases. The ensemble model achieved outstanding performance with an overall accuracy of 99.88% [95% CI: 99.6 - 100], AUC of 99.38% [95% CI: 97.5 - 100], a sensitivity of 98.72% [95% CI: 94.6 - 100] and a specificity of 99.99% [95% CI: 99.99- 100]. Discussion The proposed model revealed better performance when compared against existing state-of-the-art studies [ 3 , 22 , 56 , 71 ] for the same set of features employed by them. As compared to the best performing Bayes Net model [ 22 ] average accuracy of 95.159%, ERLX achieved an average accuracy of 99.94%. In comparison with AUC of 85% reported by the SVM model [ 56 ], ERLX obtained AUC of 99.77% in addition to improvements in sensitivity, and specificity. As compared with ER-COV model [ 71 ] average sensitivity of 70.25% and specificity of 85.98%, ERLX model achieved sensitivity of 99.47% and specificity of 99.99%. The ERLX model obtained considerable higher score as compared with ANN model [ 3 ] in all performance metrics. Therefore, the model presented is robust and can be deployed for reliable early and rapid screening of COVID-19 patients.

Containers emerged as a lightweight alternative to virtual machines (VMs) that offer better microservice architecture support. The value of the container market is expected to reach $2.7 billion in 2020 as compared to $762 million in 2016. Although they are considered the standardized method for microservices deployment, playing an important role in cloud computing emerging fields such as service meshes, market surveys show that container security is the main concern and adoption barrier for many companies. In this paper, we survey the literature on container security and solutions. We have derived four generalized use cases that should cover security requirements within the host-container threat landscape. The use cases include: (I) protecting a container from applications inside it, (II) inter-container protection, (III) protecting the host from containers, and (IV) protecting containers from a malicious or semi-honest host. We found that the first three use cases utilize a software-based solutions that mainly rely on Linux kernel features (e.g., namespaces, CGroups, capabilities, and seccomp) and Linux security modules (e.g., AppArmor). The last use case relies on hardware-based solutions such as trusted platform modules (TPMs) and trusted platform support (e.g., Intel SGX). We hope that our analysis will help researchers understand container security requirements and obtain a clearer picture of possible vulnerabilities and attacks. Finally, we highlight open research problems and future research directions that may spawn further research in this area.