ABSTRACT
The pandemic situation created by COVID'19 is ridiculous. It has made even the blood relations hide themselves from the infected person. The whole world was stunned by this situation. This is because of the uncertainty in the way in which this disease is spread. As an advancement of this disease, a few other variants like delta, omicron etc. also got spread. It is essential to find a solution to this situation. The variants Omicron and Delta are taken into consideration here. Though both the vibrant colours look alike, the symptoms and prevention methods changes for each of these vibrants. This work aims to make a study of the parameters responsible for these variants. As a result of this study, the parameters involved in the spread of these diseases are identified, and the prevention parameters are concluded. The major benefit of this comparatively study is to identify the parameters that are inconclusive, applying the concepts of fuzzy cognitive maps and neutrosophic cognitive maps is applied to bring out the result © 2023, Neutrosophic Sets and Systems.All Rights Reserved.
ABSTRACT
The k-vertex cut (k-VC) problem belongs to the family of the critical node detection problems, which aims to find a minimum subset of vertices whose removal decomposes a graph into at least k connected components. It is an important NP-hard problem with various real-world applications, e.g., vulnerability assessment, carbon emissions tracking, epidemic control, drug design, emergency response, network security, and social network analysis. In this article, we propose a fast local search (FLS) approach to solve it. It integrates a two-stage vertex exchange strategy based on neighborhood decomposition and cut vertex, and iteratively executes operations of addition and removal during the search. Extensive experiments on both intersection graphs of linear systems and coloring/DIMACS graphs are conducted to evaluate its performance. Empirical results show that it significantly outperforms the state-of-the-art (SOTA) algorithms in terms of both solution quality and computation time in most of the instances. To evaluate its generalization ability, we simply extend it to solve the weighted version of the k-VC problem. FLS also demonstrates its excellent performance. IEEE
ABSTRACT
At present, disasters frequently occur throughout the world. Due to different cultural backgrounds and organisational structures, most countries adopt network governance, hierarchical organization, and centralised management. However, the effect of management is often not satisfactory. Therefore, this paper takes the outbreak of COVID-19 in 2019 as a case to explore whether complex systems management can provide ideas to disaster response. The study demonstrates the need for complex systems in disaster response by conducting an in-depth analysis of response data in China and Australia, using the case study of the 2019 pandemic outbreak. © 2022 IEEE.
ABSTRACT
When COVID-19 Vaccines became first available, there were a lot of questions and uncertainty related to them [1]. In this paper, we aim to model the evolution of COVID-19 Vaccine Hesitancy through an opinion dynamics model. We extend the model in [5] to general polygons and explore the parameter space. We compare our model with the real data available for Floyd County, Texas. Our findings imply that our model is good in predicting evolution of Vaccine Hesitancy in other counties. We also think that our model can be used to model and predict evolution of opinions on other topics. © 2022 IEEE.
ABSTRACT
Predicting the evolution of the Covid-19 pandemic during its early phases was relatively easy as its dynamics were governed by few influencing factors that included a single dominant virus variant and the demographic characteristics of a given area. Several models based on a wide variety of techniques were developed for this purpose. Their prediction accuracy started deteriorating as the number of influencing factors and their interrelationships grew over time. With the pandemic evolving in a highly heterogeneous way across individual countries, states, and even individual cities, there emerged a need for a contextual and fine-grained understanding of the pandemic to come up with effective means of pandemic control. This paper presents a fine-grained model for predicting and controlling Covid-19 in a large city. Our approach borrows ideas from complex adaptive system-of-systems paradigm and adopts a concept of agent as the core modeling ion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Since the beginning of 2020 and following the recommendation of the Emergency Committee, the WHO (World Health Organization) Director General declared that the Covid-19 outbreak constitutes a Public Health Emergency of International Concern. Given the urgency of this outbreak, the international community is mobilizing to find ways to significantly accelerate the development of interventions. These interventions include raising awareness of ethical solutions such as wearing a face mask and respecting social distancing. Unfortunately, these solutions have been criticized and the number of infections and deaths by Covid-19 has only increased because of the lack of respect for these gestures on the one hand, and because of the lack of awareness and training tools on the spread of this disease through simulation packages on the other. To give importance to the respect of these measures, the WHO is going to try to propose to his member states, training and sensitization campaigns on coronavirus through simulation packages, so that the right decisions are taken in time to save lives. Thus, a rigorous analysis of this problem has enabled us to identify three directions for reflection. First, how to propose an IT tool based on these constraints in order to generalize training and awareness for all? Secondly, how to model and simulate these prescribed measures in our current reality? Thirdly, how to make it playful, interactive, and participative so that it is flexible according to the user’s needs? To address these questions, this paper proposes an interactive Agent-Based Model (ABM) describing a pedagogical (training and educational) tool that can help understanding the spread of Covid-19 and then show the impact of the barrier measures recommended by the WHO. The tool implemented is quite simple to use and can help make appropriate and timely decisions to limit the spread of Covid-19 in the population. © 2022,International Journal of Advanced Computer Science and Applications. All Rights Reserved.
ABSTRACT
The COVID-19 pandemic spurred the development of methodologies to assess risk to economic development plans. To increase local recovery efforts, the federal government provides funding for regional economic development. Funds are allocated based on immediate needs as well as growth potential. This work advances the risk register methodology to prioritize infrastructure initiatives - potential projects, policies, or other actions an organization may take - while considering the influence of exogenous scenarios on priorities given the impact of COVID-19. The risk register identifies performance criteria which measure performance - for example, an initiative incentivizing restaurants to increase outdoor seating improves a create new jobs criterion. Next, the register identifies disruptive events and groups these events into scenarios. There are now two sets of data: the initiatives considered for implementations, and a set of disruptive scenarios, including a baseline. The register evaluates the impact of each scenario on each initiative. For each scenario, the initiative with greatest impact on performance criteria is ranked first, and so on for the remaining scenarios. These rankings mathematically capture the influence of each scenario on the priority of each initiative. The risk register mathematically quantifies the disruptiveness of each scenario, allowing the comparison of different disruptive events. This information can help determine how to allocate resources to improve system resilience. The risk register methodology is applied to a socio-technical system of systems. This work advances methods outlined in the Systems Engineering Body of Knowledge, specifically the System of Systems knowledge area. © 2022 IEEE.
ABSTRACT
The impact of covid-19 on the financial market is considered a ‘black swan event’, i.e., the occurrence of a highly unpredictable event with far-reaching consequences. Prediction of such events in prior is essential due to the financial risk associated. In this paper, we study critical slowing down as an early warning signal to forewarn such unpredictable and sudden transitions concerning the Indian stock market for the covid-19 period. This is the first study to predict covid-19 financial crisis based on critical slowing down theory. We analyze the evolution of first-order autocorrelation and standard deviation using the sliding window approach to predict any impending transition. We found that both the early warning measures could forewarn an impending transition for almost all the stock indices considered for the analysis. © 2022, Springer Nature Switzerland AG.