The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach

Comparison of the efficacy of COVID-19 responses in South Korea and the United States

BACKGROUND

The COVID-19 pandemic devastated many countries worldwide by causing large numbers of fatalities. In our research, we wanted to answer the question: Why was there such a large difference in the mortality rate between South Korea and the United States? This is because many East Asian countries, such as Korea, had a lower mortality rate than many countries, including developed ones, across the world - the mortality rate of South Korea was about five times lower than the United States.

METHODS

This study comprehensively compares strategies used to address the COVID-19 pandemic in two different countries: South Korea and the United States. The various aspects of these two countries' responses are examined, including initial response, information dissemination and public compliance, mitigation strategies, and vaccine rollout and their impacts.

RESULTS

Early and widespread testing, rigorous contact tracing, the clear release of government information, and an organized vaccine rollout powered a proactive approach in South Korea. The United States had a contrasting response consisting of delayed and more decentralized measures, where testing lagged due to varying policies and the political controversies facing vaccine distribution.

CONCLUSIONS

We signify the gravity of rapid response and testing, clear communication, and efficient vaccine distribution, as we believe this could correlate with a lower mortality rate. In addition, we discuss future directions, including the need for a specific health infrastructure and protocol against highly infectious outbreaks.

Editorial to the Special Issue “Human Bodywork: Applications in Health, Disease, and Rehabilitation”

In this research topic, the question concerning how the human body functions through the musculoskeletal system was addressed [...]

Modeling Brain Functional Connectivity Patterns during an Isometric Arm Force Exertion Task at Different Levels of Perceived Exertion: A Graph Theoretical Approach

The perception of physical exertion is the cognitive sensation of work demands associated with voluntary muscular actions. Measurements of exerted force are crucial for avoiding the risk of overexertion and understanding human physical capability. For this purpose, various physiological measures have been used; however, the state-of-the-art in-force exertion evaluation lacks assessments of underlying neurophysiological signals. The current study applied a graph theoretical approach to investigate the topological changes in the functional brain network induced by predefined force exertion levels for twelve female participants during an isometric arm task and rated their perceived physical comfort levels. The functional connectivity under predefined force exertion levels was assessed using the coherence method for 84 anatomical brain regions of interest at the electroencephalogram (EEG) source level. Then, graph measures were calculated to quantify the network topology for two frequency bands. The results showed that high-level force exertions are associated with brain networks characterized by more significant clustering coefficients (6%), greater modularity (5%), higher global efficiency (9%), and less distance synchronization (25%) under alpha coherence. This study on the neurophysiological basis of physical exertions with various force levels suggests that brain regions communicate and cooperate higher when muscle force exertions increase to meet the demands of physically challenging tasks.

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.