ASSOCIATION RULE MINING FOR PREDICTION OF COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.

ZOOMING OUR WAY OUT OF THE FORUM NON CONVENIENS DOCTRINE

The effects of the pandemic have shed light on the evolution of technology in the legal space, including the use of technology in videoconferencing proceedings and facilitating court procedures. Despite the benefits associated with technology, the rapid adoption of videoconferencing proceedings in courts may have unprecedented impacts on the relevance and practicality of the forum non conveniens doctrine. Additionally, the drastically different approaches that federal courts have taken in response to the disproportionate geographic effects of the pandemic may give way to forum shopping. Plaintiffs may be more incentivized to bring their cases to forums that allow for videoconferencing proceedings as a strategic way to circumvent a defendant's potential forum non conveniens argument in a motion to dismiss. This Note argues that videoconferencing technology allows courts to effectively transcend the restrictions of geography while mitigating arguments about the relative convenience of different forums. Creating more uniform rules for videoconferencing proceedings will ensure easier predictability and uniformity in the forum non conveniens analysis. Specifically, this Note recommends that Congress and the courts mandate standardized technological videoconferencing requirements and adopt the original understanding of the forum non conveniens doctrine for lower courts to more explicitly consider the benefits of technology when making a forum non conveniens determination.

A Comparative Study of Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps on Covid Variants

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.

Mining association rules for classification using frequent generator itemsets in arules package

Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we propose an extension of arules package by adding the option of mining frequent generator itemsets. We discuss in detail how generators can be used for a classification task through an application example in relation with COVID-19.

Benzodiazepines and Covid-19

The prescription of benzodiazepines is governed by rules to ensure that the medication is taken in a healthy way. From the start of the Covid-19 pandemic, increased attention was paid to these rules given the respiratory risk of the disease and the drug interactions with its treatment protocols. With regard to what has been mentioned above, this update will try to give an answer to the adaptation of the prescription of benzodiazepines in Covid-19 patients. © 2022, John Libbey Eurotext. Tous droits réservés.;La prescription des benzodiazépines est régie par des règles assurant une prise saine de médicaments. Dès le début de la pandémie Covid-19 une attention plus particulière a été accordée à ces règles vu le risque respiratoire de la maladie et les interactions médicamenteuses avec ses protocoles thérapeutiques. Pour tout ce qui est cité, cette mise au point va essayer de donner une réponse à l'adaptation de la prescription des benzodiazépines chez les patients Covid-19. © 2022 John Libbey Eurotext. All rights reserved.

The skin damage in the control of COVID-2019 infections and strategy to guide the treatment in a new stage

Since the outbreak of the novel coronavirus in 2019, with the relentless efforts of the country in the early stage to the "10 new measures" now, the prevention and control has been gradually released from strict regulations. The number of COVID-19 infections increased, and wide attention has been attracted by the primary skin diseases, deterioration of pre-existing skin diseases, and other skin damage that resulted from self-protection and treatment. Considering the series of skin problems caused by COVID-19 infection and prevention measures, we mainly summarize the common skin damage after the"10 new measures" and propose a strategy to guide the treatment in this article.

Response of forest management institutions to health-related shocks. Learning from the Busitema Forest Reserve of Uganda during the COVID-19 outbreak

There is extensive literature on forest management institutional responses as a function of socio-economic and political factors, albeit limited evidence on responses triggered by health shocks. To bridge this gap, this paper analyses forest management institutional response approaches around the Busitema Forest Reserve in Uganda, using the COVID-19 pandemic as a case. Household surveys (n = 135), focus group discussions (n = 4) and key informant interviews (n = 8) provided the relevant data. The results indicate that compliance with formal and informal institutions increased during the pandemic;this was attributed to fear and uncertainty about the mode of spread of the COVID-19 virus, which was flagged by mainstream media as a zoonotic disease. Formal institutional enforcement agents, therefore, used the pandemic to forward their agenda and reinforce rules that aim to exclude local people from resource appropriation in this reserve. The response was further manifested through the transposition of existing institutions to new functions, changes in rule application and the introduction of new rules. These responses paved the way for formal institutions to tighten their control of forest resource use by allying with informal institutions. The study provides complementary evidence on institutional change with an emphasis on COVID-19 as a health-related trigger.

Assessing research on the COVID-19 pandemic and tourism published in English-language journals: 7research progress and academic contributions

This article aims to address the adequacies of the preceding review studies, which have largely failed to systematically analyze the academic contributions (notably, theoretical and methodological contributions) made by the extant studies pertinent to COVID-19 and tourism. Specifically, we have collected up to 245 articles indexed in top 10 academic journals in the field of tourism studies, including Annals of Tourism Research, Tourism Management, Journal of Travel Research, Journal of Sustainable Tourism and so forth. The keywords used for search involve "COVID-19" "COVID" "pandemic" "epidemic" "coronavirus" and "corona virus". The publication dates of the articles all fall somewhere between the start of the pandemic in January, 2020 and the 31st of August, 2021. Based upon the analysis framework proposed by authors, according to John Tribe's essay, and that formulated by Colquitt and Zapata-Phelan, this article evaluates the extent to which the sampled studies have made a contribution to the extant theories and methodology related to tourism.As the research outcomes manifest, first, the extant studies could be categorized according to their research themes. Specifically, most research shed light on tourist behaviours and the impacts of the COVID-19 pandemic on tourism development, particularly on the national and destinational levels. In contrast, very few has reflected upon the changes in tourism as a discipline, in general, and the relevant research approaches, in specific. Second, roughly half of the sample articles are quantitative studies, most of which are in favour of either questionnaires or statistics. In contrast, qualitative studies only take a lesser share. Third, with respect to academic contribution, it is clear that significant theoretical contribution is rarely made in the sampled studies. Most are found oriented to solving real-world problems. This imbalance would, perhaps, pose a threat to the growing tourism research in the long run. The reasons are manifold, but we focus upon triple key human and nonhuman factors, namely, academics, academic journals, and the rule and regulations by institutions (e.g., universities), which might have conspired to manipulate the process of (co-)producing tourism knowledge. Thus, to solve practical questions in the real time has become popular among academics, who might be increasingly reluctant to spend sufficient time and energy on theory building itself. Nevertheless, theory building, after all, is vitally significant, not least because it arguably paves a base stone for the future of tourism research. As such, we suggest that the current tourism knowledge production system needs to be reformed, encouraging more academics in future to focus on the theoretical significance of their own studies. This article has some limitations, as we only target the articles indexed in the top 10 journals in tourism. It means that our research findings might be less representative than expected. Moreover, it might be better to evaluate respectively the significance of the studies in different tourism subjects, whose fabrics might vary from one to another. In so doing, more nuanced insights might be mobilized in this aspect, providing most useful guidance to other scholars with utmost interest in the production of tourism knowledge.

Discovering COVID-19 Death Patterns from Deceased Patients: A Case Study in Saudi Arabia

COVID-19 is a serious infection that cause severe injuries and deaths worldwide. The COVID-19 virus can infect people of all ages, especially the elderly. Furthermore, elderly who have co-morbid conditions (e.g., chronic conditions) are at an increased risk of death. At the present time, no approach exists that can facilitate the characterization of patterns of COVID-19 death. In this study, an approach to identify patterns of COVID-19 death efficiently and systematically is applied by adapting the Apriori algorithm. Validation and evaluation of the proposed approach are based on a robust and reliable dataset collected from Health Affairs in the Makkah region of Saudi Arabia. The study results show that there are strong associations between hypertension, diabetes, cardiovascular disease, and kidney disease and death among COVID-19 deceased patients © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

How the Rules of no.11/POJK.03/2020 Banking Restructuring Policy Improve Financial Performance? (Empirical Study of Islamic Banks in Indonesia)

The Covid-19 pandemic has significantly impacted the economy, including the banking industry. The im-pact on the banking industry is a decline in the health of banks. One form of bank soundness assessment can be seen from the movement of financial ratios, including Non-Performing Financing (NPF), Capital Adequacy Ratio (CAR), Return on Assets (RoA), and Operational Expenditure to Operating Income (BOPO), and Financing to Deposits Ra-tio (FDR). This study aimed to examine the impact of the implementation of banking restructuring policies on the fi-nancial performance of Islamic Commercial Banks in Indonesia. This study used an observation period of 36 months, calculated 1 year before and after the implementation of rules No.11/POJK.03/2020. The sampling method used purposive sampling with 119 observational data samples. Hypothesis testing used the independent Mann-Whitney t-test since the data were not normally distributed. The results showed that the banking restructuring policy could only improve the bank's financial performance, namely CAR and FDR, but not the ratio of NPF, ROA, and BOPO. The contribution of this study can be used as one of the basics for assessing the effectiveness of implement-ing government policies. Copyright © 2022- All Rights Reserved.

Rules Determination Based on Time-Series Data to Classify Unsupervised Cases Based on Fuzzy Expert System

Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest. This is because there are multiple stages in the IFS by considering non-membership functions © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.

Extracting Temporal Relationships in EHR: Application to COVID-19 Patients.

Association rules are one of the most used data mining techniques. The first proposals have considered relations over time in different ways, resulting in the so-called Temporal Association Rules (TAR). Although there are some proposals to extract association rules in OLAP systems, to the best of our knowledge, there is no method proposed to extract temporal association rules over multidimensional models in these kinds of systems. In this paper we study the adaptation of TAR to multidimensional structures, identifying the dimension that establishes the number of transactions and how to find time relative correlations between the other dimensions. A new method called COGtARE is presented as an extension of a previous approach proposed to reduce the complexity of the resulting set of association rules. The method is tested in application to COVID-19 patients data.

Application of Data Mining to Support Facilities Management in Smart Buildings

Concerns about the effects of global warming and predicted rising sea levels are radically changing government policies to lower carbon emissions using sustainable green technologies. The United Kingdom aims to reduce its carbon emissions by 78% by 2035 and achieve net zero by 2050. This is a major driver for energy management and is influencing development of buildings which use autonomous smart technologies to assist in lowering carbon footprints. These Smart Buildings use digital technologies by connecting sensor data with intelligent systems which can be monitored remotely to provide more efficient facilities management. The data harvested and transmitted from the IoT sensors provides a key component for Big Data Analytics using techniques such as Association rule mining for intelligent interpretation which can assist facilities management becoming more agile regarding office space utilization. The shift toward hybrid working particularly instigated by the COVID-19 pandemic and recent energy supply concerns caused by the Ukraine crisis presents facilities management with opportunities to optimize their space, reduce energy consumption, and allow them to identify commercial opportunities for the unused space throughout the building. This chapter discusses the use of association rules for data mining derived from a simulated dataset for an investigative analysis of office workflow patterns for facilities management operations, resource conservation, and sustainability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Theoretical Linguistic Fuzzy Rule-Based Compartmental Modeling for the COVID-19 Pandemic

The COVID-19 pandemic has affected the whole world quite seriously. The number of new infectious cases and death cases are rapidly increasing over time. In this study, a theoretical linguistic fuzzy rule-based susceptible-exposed-infectious-isolated-recovered (SEIIsR) compartmental model has been proposed to predict the dynamics of the transmission of COVID-19 over time considering population immunity and infectiousness heterogeneity based on viral load in the model. The model's equilibrium points have been calculated, and stability analysis of the model's equilibrium points has been conducted. Consequently, the fuzzy basic reproduction number, R0f, of the fuzzy model has been formulated. Finally, the temporal dynamics of different compartmental populations with immunity and infectiousness heterogeneity using the fuzzy Mamdani model are delineated, and some disease control policies have been suggested to get over the infection in no time. Copyright © 2022, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

TRADE IN GOODS

As international trade became more sophisticated, the ‘traditional' trade in goods topics seemed to be entering an extinction phase and be substituted by other forms of trade regulation, such as technical barriers or sanitary measures. However, in recent years and for different reasons, many of them reclaimed their relevance. This chapter also discusses an essential tool for the operation of trade in goods, which is tariff classification, as well as an often overlooked but fundamental issue in trade in goods regulation, which are the rules of origin. Global value chain operations could not be conceived without rules of origin. Finally, the COVID 19 pandemic revealed how vulnerable international trade could be to import and export restrictions, such as quotas or licences, and how important trade facilitation could be to bolster trade and even to save lives. © Oxford University Press 2022. All rights reserved.

INDIVIDUAL HOME-WORK ASSIGNMENTS FOR STATE TAXES

The surge in work-from-home arrangements brought on by the COVID-19 pandemic threatens serious disruptions to state tax systems. Billions of dollars are at stake at this pivotal moment as states grapple with where to assign income earned through these remote work arrangements for tax purposes: the worker's home or the employer's location? Some states-intent on modernizing their income tax laws-have assigned such income to the employer's location, but have faced persistent challenges on both constitutional and policy grounds in response. This Article provides a vigorous defense against such challenges. The Supreme Court has long interpreted the Constitution to be deferential to state tax actions;new laws for the age of remote work surely satisfy constitutional demands. Moreover, assigning income from remote work to the employer's location is more equitable than assigning the income to the worker's home, justifying modernization efforts from a policy perspective. The solution to this homework assignment problem is evident: the states must revise their tax laws to face the evolving nature of work.

Execution of Market Basket Analysis and Recommendation Systems in Physical Retail Stores to Advance Sales Revenues

In the early months of 2020, pandemic covid-19 hit many parts of the world. Especially developing countries like India observed a negative growth rate in few quarters of last financial year. Retailing is one of the key sectors that contribute to Indian GDP with a share of nearly 10 percent. Hence there is a need for the retail sector to bounce back which is possible with the efficient use of new digital technologies. Market basket analysis is used here to extract the association rules which can be directly used for formulating discount and combo offers. Along with that, these rules can be used to decide the product positioning in the retail store. Items which are bought together can be placed next to each other to increase sales. Recommendation systems are most commonly used in ecommerce websites like Amazon, Flipkart, etc, and streaming platforms like Netflix to recommend the items that are to be purchased by users. Although recommendation engines are implemented in multiple web and mobile applications, these are not in the implementation stage in offline retail stores due to many implications associated with them like infrastructure, cost, etc. In this project, we have used market basket analysis and recommendation systems to propose a model to implement in retail stores to increase sales revenues and enhance customer experience. © 2022 IEEE.

"We sleep 10cm apart so there is no social distancing": COVID-19 preparedness in a Zimbabwean prison complex.

PURPOSE: Prisons in Africa face unprecedented challenges during Coronavirus disease 2019 (COVID-19). In July 2020, the first prison system case of COVID-19 was notified in Zimbabwe. Subsequently, the Zimbabwe Prisons and Correctional Services released their COVID-19 operational plan. The purpose of the study was to assess preparedness, prevention and control of COVID-19 in selected prisons in Zimbabwe. DESIGN/METHODOLOGY/APPROACH: A multi-method situation assessment of COVID-19 preparedness was conducted across three Zimbabwean prisons. The World Health Organization checklist to evaluate preparedness, prevention and control of COVID-19 in prisons was administered to frontline health managers. Information garnered was further explored during site observation and in multi-stakeholder key informant interviews with policymakers, prison health directorate, frontline health-care professionals, officers in charge and non-governmental organizations (n = 26); focus group discussions with correctional officers (n = 18); and male/female prisoners (n = 36). Data was triangulated and analyzed using content thematic analysis. FINDINGS: Outdated infrastructure, severe congestion, interrupted water supply and inadequate hygiene and sanitation were conducive to ill-health and spread of disease. Health professionals had been well-trained regarding COVID-19 disease control measures. COVID-19 awareness among prisoners was generally adequate. There was no routine COVID-19 testing in place, beyond thermo scanning. Access to health care was good, but standards were hindered by inadequate medicines and personnel protective equipment supply. Isolation measures were compromised by accommodation capacity issues. Flow of prison entries constituted a transmission risk. Social distancing was impossible during meals and at night. ORIGINALITY/VALUE: This unique situation assessment of Zimbabwean prisons' preparedness and approach to tackling COVID-19 acknowledges state and prison efforts to protect prisoners and staff, despite infrastructural constraints and inadequate resourcing from government.

A multi-stakeholder situation assessment of COVID-19 disease preparedness and mitigation measures in a large prison complex in Malawi.

PURPOSE: Prisons in the sub-Saharan African region face unprecedented challenges during the COVID-19 pandemic. In Malawi, the first prison system case of COVID-19 was notified in July 2020. While prison settings were included in the second domestic COVID-19 response plan within the Law Enforcement cluster (National COVID-19 preparedness and response plan, July-December 2020), they were initially not included in the K157bn (US$210m) COVID-19 fund. The purpose of the study was to assess prison preparedness, prevention and control of COVID-19 in Malawi.. DESIGN/METHODOLOGY/APPROACH: A multi-method situation assessment of the COVID-19 response and human rights assurance of prisoners and staff was conducted in a large prison complex in Malawi. Qualitative research underpinned by the Empirical Phenomenological Psychological (EPP) framework consisted of interviews with key informants such as prison health personnel, senior prison staff, penal and judicial policymakers, government and civil society organisations (n = 14) and focus group discussions with consenting male (n = 48) and female prisoners (n = 48) and prison wardens (n = 24). Prison site visits were supported by detailed observations based on the World Health Organisation Checklist for COVID-19 in prisons (n = 9). Data were collected and analysed thematically using the EPP stepwise approach and triangulated based on Bronfenbrenner's model conceptualising COVID-19 as a multi-level event disrupting the prison eco-system. FINDINGS: The results are presented as MICRO-MESO level individual and community experiences of incarceration during COVID-19 spanning several themes: awareness raising and knowledge of COVID-19 in prisons; prison congestion and the impossibility of social distancing; lack of adequate ventilation, hygiene and sanitation and provisions and correct use of personal protective equipment; MESO-MACRO level interplay between the prison community of prisoners and staff and judicial policy impacts; medical system COVID-19 response, infrastructure and access to health care; COVID-19 detection and quarantine measures and prisoner access to the outside world. ORIGINALITY/VALUE: This unique situation assessment of the Malawian prison system response to mitigate COVID-19 illustrates the dynamics at the micro-level whereby prisoners rely on the state and have restricted agency in protecting themselves from disease. This is due to severe structural inadequacies based on low resource allocation to prisons leading to a compromised ability to prevent and treat disease; an infirm and congested infrastructure and bottlenecks in the judicial system fuelling a continued influx of remand detainees leading to high overcapacity. Multi-pronged interventions involving key stakeholders, with prison management and line Ministry as coordinators are warranted to optimise COVID-19 interventions and future disease outbreaks in the Malawian prison system.

Ontology-based semantic data interestingness using BERT models

The COVID-19 pandemic has generated massive data in the healthcare sector in recent years, encouraging researchers and scientists to uncover the underlying facts. Mining interesting patterns in the large COVID-19 corpora is very important and useful for the decision makers. This paper presents a novel approach for uncovering interesting insights in large datasets using ontologies and BERT models. The research proposes a framework for extracting semantically rich facts from data by incorporating domain knowledge into the data mining process through the use of ontologies. An improved Apriori algorithm is employed for mining semantic association rules, while the interestingness of the rules is evaluated using BERT models for semantic richness. The results of the proposed framework are compared with state-of-the-art methods and evaluated using a combination of domain expert evaluation and statistical significance testing. The study offers a promising solution for finding meaningful relationships and facts in large datasets, particularly in the healthcare sector. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.