Graph data science and machine learning for the detection of COVID-19 infection from symptoms.

Background: COVID-19 is an infectious disease caused by SARS-CoV-2. The symptoms of COVID-19 vary from mild-to-moderate respiratory illnesses, and it sometimes requires urgent medication. Therefore, it is crucial to detect COVID-19 at an early stage through specific clinical tests, testing kits, and medical devices. However, these tests are not always available during the time of the pandemic. Therefore, this study developed an automatic, intelligent, rapid, and real-time diagnostic model for the early detection of COVID-19 based on its symptoms. Methods: The COVID-19 knowledge graph (KG) constructed based on literature from heterogeneous data is imported to understand the COVID-19 different relations. We added human disease ontology to the COVID-19 KG and applied a node-embedding graph algorithm called fast random projection to extract an extra feature from the COVID-19 dataset. Subsequently, experiments were conducted using two machine learning (ML) pipelines to predict COVID-19 infection from its symptoms. Additionally, automatic tuning of the model hyperparameters was adopted. Results: We compared two graph-based ML models, logistic regression (LR) and random forest (RF) models. The proposed graph-based RF model achieved a small error rate = 0.0064 and the best scores on all performance metrics, including specificity = 98.71%, accuracy = 99.36%, precision = 99.65%, recall = 99.53%, and F1-score = 99.59%. Furthermore, the Matthews correlation coefficient achieved by the RF model was higher than that of the LR model. Comparative analysis with other ML algorithms and with studies from the literature showed that the proposed RF model exhibited the best detection accuracy. Conclusion: The graph-based RF model registered high performance in classifying the symptoms of COVID-19 infection, thereby indicating that the graph data science, in conjunction with ML techniques, helps improve performance and accelerate innovations.

Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases.

Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many researchers have developed models to diagnose them in the early stages. This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis. We searched the Web of Science, ScienceDirect, PubMed, Springer, and IEEE databases from 2015 to 2022, identified the pros and cons of the reviewed ML models, and discussed the possible recommendations to advance the studies in this field. We found that most of the articles used small datasets, and few of them used real-time data. Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal. Moreover, heterogeneous data could ensure the model's generalization, while big data, many features, and a hybrid model will increase the resulting performance. Furthermore, using other techniques such as deep learning and NLP to extract vast features from unstructured data is a powerful approach to enhancing the performance of ML diagnostic models.

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging.

The world has witnessed dramatic changes because of the advent of COVID19 in the last few days of 2019. During the last more than two years, COVID-19 has badly affected the world in diverse ways. It has not only affected human health and mortality rate but also the economic condition on a global scale. There is an urgent need today to cope with this pandemic and its diverse effects. Medical imaging has revolutionized the treatment of various diseases during the last four decades. Automated detection and classification systems have proven to be of great assistance to the doctors and scientific community for the treatment of various diseases. In this paper, a novel framework for an efficient COVID-19 classification system is proposed which uses the hybrid feature extraction approach. After preprocessing image data, two types of features i.e., deep learning and handcrafted, are extracted. For Deep learning features, two pre-trained models namely ResNet101 and DenseNet201 are used. Handcrafted features are extracted using Weber Local Descriptor (WLD). The Excitation component of WLD is utilized and features are reduced using DCT. Features are extracted from both models, handcrafted features are fused, and significant features are selected using entropy. Experiments have proven the effectiveness of the proposed model. A comprehensive set of experiments have been performed and results are compared with the existing well-known methods. The proposed technique has performed better in terms of accuracy and time.

Analyzing the Nexus Between Geopolitical Risk, Policy Uncertainty, and Tourist Arrivals: Evidence From the United States.

This study attempts to explore the causal linkage of the COVID-19 pandemic, economic policy uncertainty, geopolitical risk, and tourism arrivals in the United States taking data from January to November 2020. In order to analyze the above relationship, this study uses a novel time-varying granger causality test developed by Shi et al. (2018), which incorporates its three causality algorithms such as forward recursive causality, rolling causality, and recursive evolving causality. The findings from forward recursive causality could not confirm any significant causal relationship between COVID-19 and tourism, geopolitical risk (GPR) and tourism, economic policy uncertainty and tourism, and geopolitical risk and COVID-19 but found causality between economic policy uncertainty and COVID-19. The rolling window causality reported bidirectional causality between COVID-19 and tourism and unidirectional causality running from tourism to geopolitical risk. However, the recursive evolving causality identified a significant bidirectional causal relationship between all the variables. Based on the findings, policy implications for the tourism sector are provided.

COVID-19 and stock exchange return variation: empirical evidences from econometric estimation.

This research looked at the effects of COVID-19 on a number of the world's most important stock exchanges, as well as the empirical relation between the COVID-19 wave and stock market volatility. In order to plan proper portfolio diversification in international financial markets, researchers must examine COVID-19 anxiety in relation to stock market volatility. The stock market volatility connected with the COVID-19 pandemic was measured using AR(1)-GARCH(1,1). COVID-19 fear, according to our research, is the ultimate driver of public attention and stock market volatility. The findings show that throughout the pandemic, stock market performance and GDP growth both declined significantly due to average increases. Furthermore, a 1% increase in COVID-19 causes a 0.8% and 0.56% decline in stock return and GDP, respectively. The stock market, on the other hand, showed a slight movement in GDP growth. Furthermore, the COVID-19 pandemic reported cases index, death index, and global panic index all influenced public perceptions of purchasing and selling. As a result, rather than investing in stocks, it is recommended that you invest in gold. The research also makes policy recommendations for important stakeholders. We look to examine how stock returns respond dynamically to unanticipated changes in the COVID-19 scenarios, as well as the uncertainty that comes with a pandemic. Using daily data from Canada and the USA, we conclude that a spike in COVID-19 instances has a negative impact on the stock market in general. Furthermore, in both the increase and decline scenarios in Canada, the stock return reactions are asymmetric. The disparity is due to the unfavorable impact of the pandemic's unpredictability. We also discovered that uncertainty had a negative impact on the US stock market. The magnitude, however, is insignificant.

Determination of total phenolic, flavonoid, carotenoid, and mineral contents in peel, flesh, and seeds of pumpkin (Cucurbita maxima)

Pumpkin is an important vegetable, which has potential to be used as medicinal and functional food. Not only the pulp but also the peel and seeds of pumpkin are good sources of phytochemicals and minerals. Pumpkin peel, flesh, and seeds were dried to obtain powders, and 80% of methanolic extracts were prepared for further analyses. Among three fractions of pumpkin, higher content of total phenolics (224.61 ± 1.60‐mg GAE/100‐g powder) and total flavonoids (139.37 ± 1.07‐mg CE/100‐g powder) were recorded in pumpkin seeds as compared with peel and flesh, whereas higher carotenoids (35.2 ± 0.49 mg/100‐g powder) and β‐carotene (6.18 ± 0.04 mg/100‐g powder) were present in pumpkin flesh extract, when compared with peel and seeds. Pumpkin flesh, as compared with peel and seeds, contained higher values of Na, K, and Fe (17.87 ± 0.22, 1592 ± 20.3, and 41.50 ± 0.45 mg/100‐g powder, respectively). Valuable amount of Zn (15.21 ± 0.07 mg/100‐g powder) was present in pumpkin seeds powder.Pumpkin parts (peel, flesh, and seeds) own high nutritional significance due to the presence of total phenolics, flavonoids, total carotenoids, and appreciable amount of macroelements and microelements. Organic waste generated as a result of pumpkin processing could effectively be utilized in different food products for the development of functional and medicinal foods. Notably, pumpkin seeds are high in zinc content, and in this situation of COVID‐19 pandemic, scientific community is well aware of oxidation and mediating role of zinc for activation of enzymes in the body. Phytochemicals present in pumpkin peel, flesh, and seeds can fight against antiaging and enhance immunity. These low‐cost powders from pumpkin parts can be used as a potential source of functional foods and nutraceuticals in food and medicinal industries. [ABSTRACT FROM AUTHOR] Copyright of Journal of Food Processing & Preservation is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)