Social, economic and environmental implications of the COVID-19 pandemic.

The COVID-19 pandemic led to global lockdowns that severely curtailed economic activity. In this study, we set out to examine the social, economic, and environmental ramifications of the COVID-19 pandemic. This is a rare project that will have far-reaching consequences for the field. There are five sets of issues: short-term effects on oil and economic and agricultural policies, including regulations and COP26; long-term implications of monetary and fiscal intervention and investment in green agreements on future generations; prospects for further de-globalization and its effect on climate change and nature; and intergenerational environmental consequences, including debt and polling.

Social, economic and environmental implications of the COVID-19 pandemic

The COVID-19 pandemic led to global lockdowns that severely curtailed economic activity. In this study, we set out to examine the social, economic, and environmental ramifications of the COVID-19 pandemic. This is a rare project that will have far-reaching consequences for the field. There are five sets of issues: short-term effects on oil and economic and agricultural policies, including regulations and COP26;long-term implications of monetary and fiscal intervention and investment in green agreements on future generations;prospects for further de-globalization and its effect on climate change and nature;and intergenerational environmental consequences, including debt and polling.

Effectiveness and cost-effectiveness of a personalised self-management intervention for living with long COVID: protocol for the LISTEN randomised controlled trial.

BACKGROUND: Individuals living with long COVID experience multiple, interacting and fluctuating symptoms which can have a dramatic impact on daily living. The aim of the Long Covid Personalised Self-managemenT support EvaluatioN (LISTEN) trial is to evaluate effects of the LISTEN co-designed self-management support intervention for non-hospitalised people living with long COVID on participation in routine activities, social participation, emotional well-being, quality of life, fatigue, and self-efficacy. Cost-effectiveness will also be evaluated, and a detailed process evaluation carried out to understand how LISTEN is implemented. METHODS: The study is a pragmatic randomised effectiveness and cost-effectiveness trial in which a total of 558 non-hospitalised people with long COVID will be randomised to either the LISTEN intervention or usual care. Recruitment strategies have been developed with input from the LISTEN Patient and Public Involvement and Engagement (PPIE) advisory group and a social enterprise, Diversity and Ability, to ensure inclusivity. Eligible participants can self-refer into the trial via a website or be referred by long COVID services. All participants complete a range of self-reported outcome measures, online, at baseline, 6 weeks, and 3 months post randomisation (the trial primary end point). Those randomised to the LISTEN intervention are offered up to six one-to-one sessions with LISTEN-trained intervention practitioners and given a co-designed digital resource and paper-based book. A detailed process evaluation will be conducted alongside the trial to inform implementation approaches should the LISTEN intervention be found effective and cost-effective. DISCUSSION: The LISTEN trial is evaluating a co-designed, personalised self-management support intervention (the LISTEN intervention) for non-hospitalised people living with long COVID. The design has incorporated extensive strategies to minimise participant burden and maximise access. Whilst the duration of follow-up is limited, all participants are approached to consent for long-term follow-up (subject to additional funding being secured). TRIAL REGISTRATION: LISTEN ISRCTN36407216. Registered on 27/01/2022.

An intelligent health monitoring and diagnosis system based on the internet of things and fuzzy logic for cardiac arrhythmia COVID-19 patients.

BACKGROUND: During the COVID-19 pandemic, there is a global demand for intelligent health surveillance and diagnosis systems for patients with critical conditions, particularly those with severe heart diseases. Sophisticated measurement tools are used in hospitals worldwide to identify serious heart conditions. However, these tools need the face-to-face involvement of healthcare experts to identify cardiac problems. OBJECTIVE: To design and implement an intelligent health monitoring and diagnosis system for critical cardiac arrhythmia COVID-19 patients. METHODOLOGY: We use artificial intelligence tools divided into two parts: (i) IoT-based health monitoring; and (ii) fuzzy logic-based medical diagnosis. The intelligent diagnosis of heart conditions and IoT-based health surveillance by doctors is offered to critical COVID-19 patients or isolated in remote locations. Sensors, cloud storage, as well as a global system for mobile texts and emails for communication with doctors in case of emergency are employed in our proposal. RESULTS: Our implemented system favors remote areas and isolated critical patients. This system utilizes an intelligent algorithm that employs an ECG signal pre-processed by moving through six digital filters. Then, based on the processed results, features are computed and assessed. The intelligent fuzzy system can make an autonomous diagnosis and has enough information to avoid human intervention. The algorithm is trained using ECG data from the MIT-BIH database and achieves high accuracy. In real-time validation, the fuzzy algorithm obtained almost 100% accuracy for all experiments. CONCLUSION: Our intelligent system can be helpful in many situations, but it is particularly beneficial for isolated COVID-19 patients who have critical heart arrhythmia and must receive intensive care.

Decision Support System Based on Complex Fractional Orthotriple Fuzzy 2-Tuple Linguistic Aggregation Operator

In this research, we provide tools to overcome the information loss limitation resulting from the requirement to estimate the results in the discrete initial expression domain. Through the use of 2-tuples, which are made up of a linguistic term and a numerical value calculated between [0.5,0.5), the linguistic information will be expressed. This model supports continuous representation of the linguistic data within its scope, permitting it to express any information counting received through an aggregation procedure. This study provides a novel approach to develop a linguistic multi-attribute group decision-making (MAGDM) approach with complex fractional orthotriple fuzzy 2-tuple linguistic (CFOF2TL) assessment details. Initially, the concept of a complex fractional orthotriple fuzzy 2-tuple linguistic set (CFO2TLS) is proposed to convey uncertain and fuzzy information. In the meantime, simple aggregation operators, such as CFOF2TL weighted average and geometric operators, are defined. In addition, the CFOF2TL Maclaurin's symmetric mean (CFOF2TLMSM) operators and their weighted shapes are presented, and their attractive characteristics are also discussed. A new MAGDM approach is built using the developed aggregation operators to address managing economic crises under COVID-19 with the CFOF2TL information. As a result, the effectiveness and robustness of the developed method are accompanied by an empirical example, and a comparative study is carried out by contrasting it with previous approaches.

Fermatean Cubic Fuzzy Aggregation Operators and Their Application in Multiattribute Decision-Making Problems

The purpose of aggregation methods is to convert a list of objects of a set into a single object of the same set usually by an n-arry function, so-called aggregation operator. The key features of this work are the aggregation operators, because they are based on a novel set called Fermatean cubic fuzzy set (F-CFS). F-CFS has greater spatial scope and can deal with more ambiguous situations where other fuzzy set extensions fail to support them. For this purpose, the notion of F-CFS is defined. F-CFS is the transformation of intuitionistic cubic fuzzy set (I-CFS), Pythagorean cubic fuzzy set (P-CFS), interval-valued cubic fuzzy set, and basic orthopair fuzzy set and is grounded on the constraint that "the cube of the supremum of membership plus nonmembership degree is ≤1”. We have analyzed some properties of Fermatean cubic fuzzy numbers (F-CFNs) as they are the alteration of basic properties of I-CFS and P-CFS. We also have defined the score and deviation degrees of F-CFNs. Moreover, the distance measuring function between two F-CFNs is defined which shows the space between two F-CFNs. Based on this notion, the aggregation operators namely Fermatean cubic fuzzy-weighted averaging operator (F-CFWA), Fermatean cubic fuzzy-weighted geometric operator (F-CFWG), Fermatean cubic fuzzy-ordered-weighted averaging operator (F-CFOWA), and Fermatean cubic fuzzy-ordered-weighted geometric operator (F-CFOWG) are developed. Furthermore, the notion is applied to multiattribute decision-making (MADM) problem in which we presented our objectives in the form of F-CFNs to show the effectiveness of the newly developed strategy.

Correlation coefficient measures and aggregation operators on interval-valued linear Diophantine fuzzy sets and their applications

Intuitionistic fuzzy sets, Pythagorean fuzzy sets, and q-rung orthopair fuzzy sets are rudimentary concepts in computational intelligence, which have a myriad of applications in fuzzy system modeling and decision-making under uncertainty. Nevertheless, all these notions have some strict restrictions imposed on the membership and non-membership grades (e.g., the sum of the grades or the sum of the squares of the grades or the sum of the qth power of the grades is less than or equal to 1). To relax these restrictions, linear Diophantine fuzzy set is a new extension of fuzzy sets, by additionally considering reference/control parameters. Thereby, the sum of membership grade and non-membership grade can be greater than 1, and even both of these grades can be 1. By selecting different pairs of reference parameters, linear Diophantine fuzzy sets can naturally categorize concerned problems and produce appropriate solutions accordingly. In this paper, the interval-valued linear Diophantine fuzzy set, which is a generalization of linear Diophantine fuzzy set, is studied. The interval-valued linear Diophantine fuzzy set is more efficient to deal with uncertain and vague information due to its flexible intervals of membership grades, non-membership grades, and reference parameters. Some basic operations on interval-valued linear Diophantine fuzzy sets are presented. We define interval-valued linear Diophantine fuzzy weighted average and interval-valued linear Diophantine fuzzy weighted geometric aggregation operators. Based on these new aggregation operators, we propose a method for multi-criteria decision-making based on supplier selection under the interval-valued linear Diophantine fuzzy environment. Besides, a real-life example, comparison study, and advantages of proposed aggregation operators are presented. We describe some correlation coefficient measures (type-1 and type-2) for the interval-valued linear Diophantine fuzzy sets and they are applied in medical diagnosis for Coronavirus Disease 2019 (COVID-19). Lastly, a comparative examination and the benefits of proposed correlation coefficient measures are also discussed.

Case report: A brief description of unseen facts in the comorbid COVID-19 patients (preprint)

Data on two deceased individuals with COVID-19 and comorbidities such as hepatitis C, chronic kidney disease, diabetes mellitus type 2 and hypertension are discussed. Changes in laboratory signatures with impact on COVID-19 severity in both cases indicate the need for extensive monitoring of comorbid individuals to reduce morbidity and mortality.

Application of piecewise fractional differential equation to COVID-19 infection dynamics.

We proposed a new mathematical model to study the COVID-19 infection in piecewise fractional differential equations. The model was initially designed using the classical differential equations and later we extend it to the fractional case. We consider the infected cases generated at health care and formulate the model first in integer order. We extend the model into Caputo fractional differential equation and study its background mathematical results. We show that the fractional model is locally asymptotically stable when R 0 < 1 at the disease-free case. For R 0 ≤ 1 , we show the global asymptotical stability of the model. We consider the infected cases in Saudi Arabia and determine the parameters of the model. We show that for the real cases, the basic reproduction is R 0 ≈ 1 . 7372 . We further extend the Caputo model into piecewise stochastic fractional differential equations and discuss the procedure for its numerical simulation. Numerical simulations for the Caputo case and piecewise models are shown in detail.

Application of fractional optimal control theory for the mitigating of novel coronavirus in Algeria.

In this paper, we investigate the dynamics of novel coronavirus infection (COVID-19) using a fractional mathematical model in Caputo sense. Based on the spread of COVID-19 virus observed in Algeria, we formulate the model by dividing the infected population into two sub-classes namely the reported and unreported infective individuals. The existence and uniqueness of the model solution are given by using the well-known Picard-Lindelöf approach. The basic reproduction number R 0 is obtained and its value is estimated from the actual cases reported in Algeria. The model equilibriums and their stability analysis are analyzed. The impact of various constant control parameters is depicted for integer and fractional values of α . Further, we perform the sensitivity analysis showing the most sensitive parameters of the model versus R 0 to predict the incidence of the infection in the population. Further, based on the sensitivity analysis, the Caputo model with constant controls is extended to time-dependent variable controls in order obtain a fractional optimal control problem. The associated four time-dependent control variables are considered for the prevention, treatment, testing and vaccination. The fractional optimality condition for the control COVID-19 transmission model is presented. The existence of the Caputo optimal control model is studied and necessary condition for optimality in the Caputo case is derived from Pontryagin's Maximum Principle. Finally, the effectiveness of the proposed control strategies are demonstrated through numerical simulations. The graphical results revealed that the implantation of time-dependent controls significantly reduces the number of infective cases and are useful in mitigating the infection.

COVID-19: Clinical laboratory diagnosis and monitoring of novel coronavirus infected patients using molecular, serological and biochemical markers: A review.

COVID-19, a novel coronavirus disease, has provoked a variety of health and safety concerns, and socioeconomic challenges around the globe. The laboratory diagnosis of SARS-CoV-2 was quickly established utilizing nucleic acid amplification techniques (NAAT) after the disease causing virus has been identified, and its genetic sequence has been determined. In addition to NAAT, serological tests based on antibodies testing against SARS-CoV-2 were introduced for diagnostic and epidemiologic studies. Other biochemical investigations include monitoring of peripheral blood cells count, platelets/lymphocyte ratio, coagulation profile, cardiac, and inflammatory markers such as cytokines storm are also crucial in combating COVID-19 pandemic. Further, accurate and reliable laboratory results for SARS-CoV-2 play very important role in the initiation of early treatment and timely management of COVID-19 patients, provide support in clinical decision-making process to control infection, and detection of asymptomatic cases. The Task Force on Coronavirus-19 constituted by International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) has recognized informational framework for epidemiology, pathogenesis, and recommended the PCR-based analysis, serological and biochemical assays for analysis, monitoring, and management of disease. This literature review provides an overview of the currently used diagnostic techniques in clinical laboratories for the diagnosis, treatment monitoring, and management of COVID-19 patients. We concluded that each assays differ in their performance characteristics and the utilization of multiple techniques is necessary for the accurate diagnosis and management of SARS-CoV-2 infection.

Dexamethasone : a corticosteroid drug for the treatment of coronavirus disease 2019

The corticosteroid drug “dexamethasone” has been in use since 1960s for reducing inflammation in a variety of conditions such as certain cancers and other inflammatory disorders. It is an affordable agent and currently off-patent in most countries and listed in multiple formulations since 1977 in the World Health Organization model list of essential medicines. The cytokines production and damaging effect has been limited through the use of dexamethasone and this will also block B cells from antibodies production and inhibit the T cell's protective function potential leading to elevated viral load in the plasma that persists for longer time after a patient survives SARS. In addition, dexamethasone would chunk the macrophages from clearing the resultant nosocomial infections. Thus, dexamethasone may be valuable for the immediate relief in severe cases of COVID-19, but could be dangerous on the long run as the body will be barred from producing protective antibodies in addition to the persistence of the virus.

Hierarchical medical diagnosis approach for COVID-19 based on picture fuzzy fairly aggregation operators

In Pakistan, a hierarchical healthcare system is an efficient way of addressing the issue of limited and insufficient healthcare services. Identifying the various degrees of disease based on the doctor’s diagnosis is an important step in developing the hierarchical healthcare treatment structure. This research presents a framework for dealing with the issue of diagnosis values presented as “picture fuzzy numbers (PFNs)”. Specifically, the goal of this study is to establish some innovative operational laws and “aggregation operators” (AOs) in a picture fuzzy environment. In this regard, we proposed some new neutral or fair operational laws that incorporate the concept of proportional distribution in order to achieve a neutral or fair remedy to the positive, neutral and negative aspects of PFNs. Based on the developed operational laws, we proposed the “picture fuzzy fairly weighted average operator” and the “picture fuzzy fairly ordered weighted averaging operator”. Compared to previous techniques, the proposed AOs provide more generalized and reliable. Furthermore, using proposed AOs with multiple decision-makers and partial weight information under PFNs, a “multi-criteria decision-making” algorithm is developed. Finally, we provide an example to show how the novel approach can aid hierarchical treatment systems. This is essential for merging the healthcare capabilities of the general public and optimizing the medical care system’s service performance. [ FROM AUTHOR] Copyright of International Journal of Biomathematics is the property of World Scientific Publishing Company 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 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 . (Copyright applies to all s.)

Interval-Valued Linear Diophantine Fuzzy Frank Aggregation Operators with Multi-Criteria Decision-Making

We introduce the notion of the interval-valued linear Diophantine fuzzy set, which is a generalized fuzzy model for providing more accurate information, particularly in emergency decision-making, with the help of intervals of membership grades and non-membership grades, as well as reference parameters that provide freedom to the decision makers to analyze multiple objects and alternatives in the universe. The accuracy of interval-valued linear Diophantine fuzzy numbers is analyzed using Frank operations. We first extend the Frank t-conorm and t-norm (FTcTn) to interval-valued linear Diophantine fuzzy information and then offer new operations such as the Frank product, Frank sum, Frank exponentiation, and Frank scalar multiplication. Based on these operations, we develop novel interval-valued linear Diophantine fuzzy aggregation operators (AOs), including the “interval-valued linear Diophantine fuzzy Frank weighted averaging operator and the interval-valued linear Diophantine fuzzy Frank weighted geometric operator”. We also demonstrate various features of these AOs and examine the interactions between the proposed AOs. FTcTns offer two significant advantages. Firstly, they function in the same way as algebraic, Einstein, and Hamacher t-conorms and t-norms. Secondly, they have an additional parameter that results in a more dynamic and reliable aggregation process, making them more effective than other general t-conorm and t-norm approaches. Furthermore, we use these operators to design a method for dealing with multi-criteria decision-making with IVLDFNs. Finally, a numerical case study of the novel carnivorous issue is shown as an application for emergency decision-making based on the proposed AOs. The purpose of this numerical example is to demonstrate the practicality and viability of the provided AOs.

Real-time artificial intelligence based health monitoring, diagnosing and environmental control system for COVID-19 patients.

By upgrading medical facilities with internet of things (IoT), early researchers have produced positive results. Isolated COVID-19 patients in remote areas, where patients are not able to approach a doctor for the detection of routine parameters, are now getting feasible. The doctors and families will be able to track the patient's health outside of the hospital utilizing sensors, cloud storage, data transmission, and IoT mobile applications. The main purpose of the proposed research-based project is to develop a remote health surveillance system utilizing local sensors. The proposed system also provides GSM messages, live location, and send email to the doctor during emergency conditions. Based on artificial intelligence (AI), a feedback action is taken in case of the absence of a doctor, where an automatic injection system injects the dose into the patient's body during an emergency. The significant parameters catering to our project are limited to ECG monitoring, SpO2 level detection, body temperature, and pulse rate measurement. Some parameters will be remotely shown to the doctor via the Blynk application in case of any abrupt change in the parameters. If the doctor is not available, the IoT system will send the location to the emergency team and relatives. In severe conditions, an AI-based system will analyze the parameters and injects the dose.

Application of fractional optimal control theory for the mitigating of novel coronavirus in Algeria

In this paper, we investigate the dynamics of novel coronavirus infection (COVID-19) using a fractional mathematical model in Caputo sense. Based on the spread of COVID-19 virus observed in Algeria, we formulate the model by dividing the infected population into two sub-classes namely the reported and unreported infective individuals. The existence and uniqueness of the model solution are given by using the well-known Picard-Lindelöf approach. The basic reproduction number

Implementation of Omni-D Tele-Presence Robot Using Kalman Filter and Tricon Ultrasonic Sensors

The tele-presence robot is designed to set forth an economic solution to facilitate day-to-day normal activities in almost every field. There are several solutions to design tele-presence robots, e.g., Skype and team viewer, but it is pretty inappropriate to use Skype and extra hardware. Therefore, in this article, we have presented a robust implementation of the tele-presence robot. Our proposed omnidirectional tele-presence robot consists of (i) Tricon ultrasonic sensors, (ii) Kalman filter implementation and control, and (iii) integration of our developed WebRTC-based application with the omnidirectional tele-presence robot for video transmission. We present a new algorithm to encounter the sensor noise with the least number of sensors for the estimation of Kalman filter. We have simulated the complete model of robot in Simulink and Matlab for the tough paths and critical hurdles. The robot successfully prevents the collision and reaches the destination. The mean errors for the estimation of position and velocity are 5.77% and 2.04%. To achieve efficient and reliable video transmission, the quality factors such as resolution, encoding, average delay and throughput are resolved using the WebRTC along with the integration of the communication protocols. To protect the data transmission, we have implemented the SSL protocol and installed it on the server. We tested three different cases of video resolutions (i.e., 320×280, 820×460 and 900×590) for the performance evaluation of the video transmission. For the highest resolution, our TPR takes 3.5 ms for the encoding, and the average delay is 2.70 ms with 900 × 590 pixels.

Assessing the potential impact of COVID-19 Omicron variant: Insight through a fractional piecewise model.

We consider a new mathematical model for the COVID-19 disease with Omicron variant mutation. We formulate in details the modeling of the problem with omicron variant in classical differential equations. We use the definition of the Atangana-Baleanu derivative and obtain the extended fractional version of the omicron model. We study mathematical results for the fractional model and show the local asymptotical stability of the model for infection-free case if R 0 < 1 . We show the global asymptotically stable of the model for the disease free case when R 0 ≤ 1 . We show the existence and uniqueness of solution of the fractional model. We further extend the fractional order model into piecewise differential equation system and give a numerical algorithm for their numerical simulation. We consider the real cases of COVID-19 in South Africa of the third wave March 2021-Sep 2021 and estimate the model parameters and get R 0 ≈ 1 . 4004 . The real parameters values are used to show the graphical results for the fractional and piecewise model.

Fractal-fractional operator for COVID-19 (Omicron) variant outbreak with analysis and modeling.

The fractal-fraction derivative is an advanced category of fractional derivative. It has several approaches to real-world issues. This work focus on the investigation of 2nd wave of Corona virus in India. We develop a time-fractional order COVID-19 model with effects of disease which consist system of fractional differential equations. Fractional order COVID-19 model is investigated with fractal-fractional technique. Also, the deterministic mathematical model for the Omicron effect is investigated with different fractional parameters. Fractional order system is analyzed qualitatively as well as verify sensitivity analysis. The existence and uniqueness of the fractional-order model are derived using fixed point theory. Also proved the bounded solution for new wave omicron. Solutions are derived to investigate the influence of fractional operator which shows the impact of the disease on society. Simulation has been made to understand the actual behavior of the OMICRON virus. Such kind of analysis will help to understand the behavior of the virus and for control strategies to overcome the disseise in community.

Novel Concepts of q-Rung Orthopair Fuzzy Topology and WPM Approach for Multicriteria Decision-Making

A q-rung orthopair fuzzy set (q-ROFS) is a robust approach for fuzzy modeling, computational intelligence, and multicriteria decision-making (MCDM) problems. The aim of this paper is to study the topological structure on q-ROFSs and define the idea of q-rung orthopair fuzzy topology (q-ROF topology). The characterization of q-ROF α-continuous mappings between q-ROF topological spaces and q-ROF connectedness is investigated. Some relationships of different types of q-rung orthopair fuzzy connectedness are also investigated. Additionally, the “q-rung orthopair fuzzy weighted product model” (q-ROF WPM) is developed for MCDM of a hierarchical healthcare system. Due to limited and insufficient resources, a hierarchical healthcare system (HHS) is very effective to deal with the increasing problems of healthcare. Recognizing the stage of a disease with the symptoms, ranking the critical condition of patients, and referring patients to feasible hospitals are key features of HHS. A HHS will provide healthcare services in three levels, a primary health centers for initial stage of disease, secondary hospitals for secondary stage of disease, and tertiary hospital for the third-order stage. A numerical example is illustrated to demonstrate the efficiency of q-ROF WPM and advantages of HHS.