Predicting the outbreak of epidemics using a network-based approach

The spread of epidemics is a common societal problem across the world. Can operational research be used to predict such outbreaks? While equation-based approaches are used to model the trajectory of epidemics, can a network-based approach also be used? This paper presents an innovative application of epidemic modelling through the design of both approaches and compares between the two. The network-based approach proposed in this paper allows implementing heterogeneity at the level of individuals and incorporates flexibility in the variety of situations the model can be applied to. In contrast to the equation-based approach, the network-based approach can address the role of individual differences, network properties, and patterns of social contacts responsible for the spread of epidemics but are much more complex to implement. In this paper, we simulated the spread of infection at the beginning of Covid-19 (Coronavirus disease 2019) using both approaches. The results are showcased using empirical data for eight countries. Sophisticated measures, including partial curve mapping, are used to compare the simulated results with the actual number of infections. We find that the plots generated by the network-based approach match the empirical data better than the equation-based approach. While both approaches can be used to predict the spread of infections, we conclusively show that the proposed network-based approach is better suited with its ability to model the spread of epidemics at the level of an individual. Hence, this can be a model of choice for epidemiologists who are interested to model the spread of an epidemic. © 2023 Elsevier B.V.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19.

BACKGROUND: COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed. PURPOSE: This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases. METHODS: A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles. RESULTS: In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks. CONCLUSION: Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Network Load Balancing in Teleconferencing Systems

Load balancing techniques are useful for efficient networking systems. In teleconferencing systems, it is not an easy job to balance the loads and obtain efficient performance. The current study tries to suggest a network-based approach for load balancing in teleconferencing systems. The aim is to make use of the concepts of graph theory in practicing and simulating teleconferencing systems. In the suggested approach, each computer in the network is considered as a vertex, an edge will be created between two vertices if they are accessible to each other. The weight of the edge between the two computers specifies the cost of access from one vertex to another. The task of transferring happens between the shortest ways of the two nodes taking into consideration the deadline time of the tasks. In terms of the number of the missed deadline tasks, the proposed approach reflected effectiveness in comparison with other approaches. Using the proposed, it is guarantee to obtain a smooth conferencing among users, which is beneficial for the teleconferencing and e-learning as well. Finally, this proposed method is useful for securing smooth conferencing during further lockdown situation (i.e., COVID situation). © 2022 IEEE.

Quantifying Mobility Perturbation in America’s Cities during COVID-19: A Network-Based Approach

The pandemic of COVID-19 has caused severe disruptions in urban lives. Understanding and quantifying these disruptions is important to inform the development of targeted and effective measures to control the pandemic and its impact. One way of achieving this object is to measure the urban mobility perturbation caused by the pandemic. In this study, we built mobility-based networks for seven major metropolitan statistical areas (MSAs) across the United States in the years of 2019 and 2020, respectively. We quantified the disruptions of urban mobility by computing and comparing a set of network-based metrics before and during the pandemic. The proposed approach is able to uncover the impact of COVID-19 in cities and provides new insights into the resilience of cities when facing large-scale disasters. © 2021 Computing in Civil Engineering 2021 - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2021. All rights reserved.

A Densely Interconnected Convolutional Neural Network-Based Approach to Identify COVID-19 from Chest X-ray Images

The novel Corona Virus (COVID-19) has spread so rapidly that cause a devastating effect on public well-being and create an emergency around the world. Hence, the rapid identification of COVID-19 has become a challenging work within a short period. Clinical trials of patients with COVID-19 have shown that most of the patients affected by COVID-19 experience lung infection that can cause inflammation in the lung after virus-contiguity. It can damage the cells and tissue that is inside the lung. However, pneumonia is also a lung infection that can cause inflammation in the air sacs inside the lung. Chest X-rays and CT scans perform an essential role in the detection of lung-related illnesses. Therefore, concerning the diagnosis of COVID-19, radiography and chest CT are considered as fundamental imaging approaches. This study presents a densely interconnected convolutional neural network-based approach to identify COVID-19, Pneumonia and Normal patients from chest X-ray images. To experiment with the proposed methodology, a new dataset is generated by combining two different datasets from Kaggle named COVID-19 Radiography Database and Chest X-ray (COVID-19 & Pneumonia). The dataset comprises of 500 X-ray images of COVID-19 affected people, 2600 X-ray images of Normal people, and 3418 X-ray images of pneumonia affected people. The proposed densely interconnected convolutional neural network model produces 99% testing accuracy for COVID-19, 98% testing accuracy for Pneumonia and 98% testing accuracy for Normal people without the application of any augmentation techniques. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.