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The study explores the dynamics of a COVID-19 epidemic in multiple susceptible populations, including the various stages of vaccination administration. In the model, there are eight human compartments: completely susceptible;susceptible with dose-1 vaccination;susceptible with dose-2 vaccination;susceptible with booster dose vaccination;exposed;infected with and without symptoms, and recovered compartments. The biological feasibility of the model is analysed. The threshold value, R0, is derived using the next-generation matrix. The stability analysis of the equilibrium points was performed locally and globally using the threshold parameter of the model. The conditions determining disease persistence is obtained. The model is subjected to sensitivity analysis, and the most sensitive parameters are identified. Also, MATLAB is used to verify the mathematical outcomes of the system's dynamic behaviour and suggests that necessary steps should be taken to keep the spread of the omicron variant infectious disease under control. The findings of this study could aid health officials in their efforts to combat the spread of COVID-19. © 2022 International Association for Mathematics and Computers in Simulation (IMACS)
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The world experienced the life-threatening COVID-19 disease worldwide since its inversion. The whole world experienced difficult moments during the COVID-19 period, whereby most individual lives were affected by the disease socially and economically. The disease caused millions of illnesses and hundreds of thousands of deaths worldwide. To fight and control the COVID-19 disease intensity, mathematical modeling was an essential tool used to determine the potentiality and seriousness of the disease. Due to the effects of the COVID-19 disease, scientists observed that vaccination was the main option to fight against the disease for the betterment of human lives and the world economy. Unvaccinated individuals are more stressed with the disease, hence their body's immune system are affected by the disease. In this study, the SVEIHR deterministic model of COVID-19 with six compartments was proposed and analyzed. Analytically, the next-generation matrix method was used to determine the basic reproduction number (R0). Detailed stability analysis of the no-disease equilibrium (E0) of the proposed model to observe the dynamics of the system was carried out and the results showed that E0 is stable if R0<1 and unstable when R0>1. The Bayesian Markov Chain Monte Carlo (MCMC) method for the parameter identifiability was discussed. Moreover, the sensitivity analysis of R0 showed that vaccination was an essential method to control the disease. With the presence of a vaccine in our SVEIHR model, the results showed that R0=0.208, which means COVID-19 is fading out of the community and hence minimizes the transmission. Moreover, in the absence of a vaccine in our model, R0=1.7214, which means the disease is in the community and spread very fast. The numerical simulations demonstrated the importance of the proposed model because the numerical results agree with the sensitivity results of the system. The numerical simulations also focused on preventing the disease to spread in the community. © 2022 The Authors
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In this paper, a mathematical model with a standard incidence rate is proposed to assess the role of media such as facebook, television, radio and tweeter in the mitigation of the outbreak of COVID-19. The basic reproduction number R0 which is the threshold dynamics parameter between the disappearance and the persistence of the disease has been calculated. And, it is obvious to see that it varies directly to the number of hospitalized people, asymptomatic, symptomatic carriers and the impact of media coverage. The local and the global stabilities of the model have also been investigated by using the Routh-Hurwitz criterion and the Lyapunov's functional technique, respectively. Furthermore, we have performed a local sensitivity analysis to assess the impact of any variation in each one of the model parameter on the threshold R0 and the course of the disease accordingly. We have also computed the approximative rate at which herd immunity will occur when any control measure is implemented. To finish, we have presented some numerical simulation results by using some available data from the literature to corroborate our theoretical findings.
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Using the standard SIR model with three unknown biological parameters, the COVID-19 pandemic in Iraq has been studied. The least squares method and real data on confirmed infections, deaths, and recoveries over a long time (455 days) were used to estimate these parameters. In this regards, first, we find the basic reproductive number R0 is 0.9422661124 which indicates and predicts that the COVID-19 pandemic in Iraq will gradually subside until it is eradicated permanently with time. Additionally, we develop an optimal vaccination strategy with the goal of reducing COVID-19 infections and preventing their spread in Iraq, thereby putting a clear picture of control this pandemic.
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Sensitivity Analysis is a method to determine how possible changes or errors in parameter values affect model outputs. This study evaluates the AHP based decision support system used for supplier selection in a glove manufacturing industry by performing a sensitivity analysis. Uniqueness of this study is that it deals with the sensitivity analysis of criteria used for supplier selection during COVID pandemic. The pandemic has altered the weightages of factors considered for supplier selection in a normal times. Expert Choice software is used to analyze the sensitivity of these parameters. The study facilitates the decision maker to understand and experiment on the effect of criterion weights on ranking of the suppliers. This makes the decision maker confident about the decisions in both favorable and unfavorable conditions. © 2022
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Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers' reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. © 2022 Elsevier Ltd
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In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty. © 2022 The Authors
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Mathematical modeling is one of the most used techniques for analyzing and preventing the transmission of COVID-19. To control this pandemic, it is essential to classify the infected population. So in this article, a new SEAIQHRDP model was formulated to investigate the transmittal dynamics of COVID-19. This model contains nine compartments Susceptible(S) class, Exposed(E) class, Asymptomatic(A) class, Infected(I) class, Quarantined(Q) class, Hospitalized(H) class, Recovered(R) class, Death(D) class, and Insusceptible (P) class. This model was fitted to the daily and cumulative confirmed COVID-19 cases in the period between 30th January 2020 and 13th January 2021 in India. Sensitivity analysis concerning R0 was performed to classify the significance of parameters. Contour plots for R0 were executed and the effect of various parameters on the infected classes had shown graphically. The necessity of stringent face mask usage and social seclusion is highlighted by optimal control analysis as a key factor in the dramatic reduction of infection rates. So the optimal control technique was adopted to lessen the disease mortality by taking both nonpharmaceutical and pharmaceutical intervention strategies as control functions and comparing infectives and recoveries with and without controls. © 2023, Eudoxus Press, LLC. All rights reserved.
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Recently, a large portion of the world's population has experienced an unprecedented devastating effect of the COVID-19 pandemic. At the time of its outbreak, not much was known about this disease and therefore, quarantine and social distancing were the only ways suggested to prevent its spread among humans. Although the current situation is much better than before however, strict social distancing norms as well as frequent long-lasting lockdowns with stringent guidelines and actions to control the spread in the early days have affected the physical and psychological health of the people. Consequently, this study was carried out to attain the following major objectives: (i) to identify the potential psychological problems/factors that might have been caused due to COVID-19 led social distancing and lockdowns, and (ii) to determine the ranks of the identified psychological factors to reflect their degree of criticality. The first objective was achieved by gathering information about the potential psychological factors from the experts. Data, in terms of linguistic variables, was collected from the experts and analyzed using two fuzzy-based multi-criteria decision-making (MCDM) methods i.e. Fuzzy Best Worst Method (F-BWM) and Fuzzy TOPSIS (F-TOPSIS) which led to the accomplishment of the second objective. The results of this study revealed that anxiety, stress, panic attacks, frustration, and insomnia were the top five critical psychological factors that might have affected people due to this pandemic. Consistency of the results was ensured by comparing the obtained ranks with the ranks found using the Fuzzy WSM and Fuzzy MABAC methods. In addition, the robustness of the results was ascertained by conducting the sensitivity analysis. Based on the findings of the study, the identified factors were categorized into most, average, and least critical psychological factors. This research might help the relevant authorities to understand the extent of the seriousness of the various psychological factors caused by this pandemic, so that an effective strategy may be developed for better management, control, and safety. © 2022 The Authors
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IntroductionCoronavirus transmission is strongly influenced by human mobilities and interactions within and between different geographical regions. Human mobility within and between cities is motivated by several factors, including employment, cultural-driven, holidays, and daily routines. MethodWe developed a sustained metapopulation (SAMPAN) model, an agent-based model (ABM) for simulating the effect of individual mobility and interaction behavior on the spreading of COVID-19 viruses across main cities on Java Island, Indonesia. The model considers social classes and social mixing affecting the mobility and interaction behavior within a sub-population of a city in the early pandemic. Travelers' behavior represents the mobility among cities from central cities to other cities and commuting behavior from the surrounding area of each city. ResultsLocal sensitivity analysis using one factor at a time was performed to test the SAMPAN model, and we have identified critical parameters for the model. While validation was carried out for the Jakarta area, we are confident in implementing the model for a larger area with the concept of metapopulation dynamics. We included the area of Bogor, Depok, Bekasi, Bandung, Semarang, Surakarta, Yogyakarta, Surabaya, and Malang cities which have important roles in the COVID-19 pandemic spreading on this island. DiscussionOur SAMPAN model can simulate various waves during the first year of the pandemic caused by various phenomena of large social mobilities and interactions, particularly during religious occasions and long holidays.
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Ethnopharmacological relevance: Scutellaria baicalensis Georgi. contains varieties of function compounds, and it has been used as traditional drug for centuries. Baicalein is the highest amount of flavonoid found in Scutellaria baicalensis Georgi., which exerts various pharmacological activities and might be a promising drug to treat COVID-19. Aim of the study: The present work aims to investigate the metabolism of baicalein in humans after oral administration, and study the pharmacokinetics of BA and its seven metabolites in plasma and urine. Materials and methods: The metabolism profiling and the identification of baicalein metabolites were performed on HPLC-Q-TOF. Then a column-switching method named MPX™-2 system was applied for the high-throughput quantificationof BA and seven metabolites. Results: Seven metabolites were identified using HPLC-Q-TOF, including sulfate, glucuronide, glucoside, and methyl-conjugated metabolites. Pharmacokinetic study found that BA was extensively metabolized in vivo, and only 5.65% of the drug remained intact in the circulatory system after single dosing. Baicalein-7-O-sulfate and baicalein-6-O-glucuronide-7-O-glucuronide were the most abundant metabolites. About 7.2% of the drug was excreted through urine and mostly was metabolites. Conclusion: Seven conjugated metabolites were identified in our assay. A high-throughput HPLC-MS/MS method using column switch was established for quantifying BA and its metabolites. The method has good sensitivity and reproducibility, and successfully applied for the clinical pharmacokinetic study of baicalein and identified metabolites. We expect that our results will provide a metabolic and pharmacokinetic foundation for the potential application of baicalein in medicine. © 2022
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Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools. We illustrate the approach by applying it to a representative of the family of susceptible-infectious-recovered (SIR) models recently used in the context of the COVID-19 pandemic. We focus on data of the early pandemic progression in Italy and the United States (the U.S.). We perform the analysis for both cases of correlated and uncorrelated inputs. Results show that quarantine rate and intervention time are the key uncertainty drivers, have opposite effects on the number of total infected individuals and are involved in the most relevant interactions.
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To understand dynamics of the COVID-19 disease realistically, a new SEIAPHR model has been proposed in this article where the infectious individuals have been categorized as symptomatic, asymptomatic, and super-spreaders. The model has been investigated for existence of a unique solution. To measure the contagiousness of COVID-19, reproduction number R 0 is also computed using next generation matrix method. It is shown that the model is locally stable at disease-free equilibrium point when R 0 < 1 and unstable for R 0 > 1 . The model has been analyzed for global stability at both of the disease-free and endemic equilibrium points. Sensitivity analysis is also included to examine the effect of parameters of the model on reproduction number R 0 . A couple of optimal control problems have been designed to study the effect of control strategies for disease control and eradication from the society. Numerical results show that the adopted control approaches are much effective in reducing new infections.
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Background: The long-term cardiovascular outcomes in COVID-19 survivors remain largely unclear. The aim of this study was to investigate the long-term cardiovascular outcomes in COVID-19 survivors. Method(s): This study used the data from the US Collaborative Network in TriNetX. From a cohort of more than 42 million records between January 1, 2019 and March 31, 2022, a total of 4 131 717 participants who underwent SARS-CoV- 2 testing were recruited. Study population then divided into two groups based on COVID-19 test results. To avoid reverse causality, the follow-up initiated 30 days after the test, and continued until 12 months. Hazard ratios (HRs) and 95% Confidence intervals (CIs) of the incidental cardiovascular outcomes were calculated between propensity score-matched patients with versus without SARS-CoV- 2 infection. Subgroup analyses on sex, and age group were also conducted. Sensitivity analyses were performed using different network, or stratified by hospitalization to explore the difference of geography and severity of COVID-19 infection. Result(s): The COVID-19 survivors were associated with increased risks of cerebrovascular diseases, such as stroke (HR [95% CI] = 1.618 [1.545-1.694]), arrhythmia related disorders, such as atrial fibrillation (HR [95% CI] = 2.407 [2.296-2.523]), inflammatory heart disease, such as myocarditis (HR [95% CI] = 4.406 [2.890-6.716]), ischemic heart disease (IHD), like ischemic cardiomyopathy (HR [95% CI] = 2.811 [2.477-3.190]), other cardiac disorders, such as heart failure (HR [95% CI] = 2.296 [2.200-2.396]) and thromboembolic disorders (e.g. pulmonary embolism: HR [95% CI] = 2.648 [2.443-2.870]). The risks of two composite endpoints, major adverse cardiovascular event (HR [95% CI] = 1.871 [1.816-1.927]) and any cardiovascular outcome (HR [95% CI] = 1.552 [1.526-1.578]), were also higher in the COVID-19 survivors than in the controls. Moreover, the survival probability of the COVID-19 survivors dramatically decreased in all the cardiovascular outcomes. The risks of cardiovascular outcomes were evident in both male and female COVID-19 survivors. Furthermore, the risk of mortality was higher in the elderly COVID-19 survivors (age >= 65 years) than in the young ones. Sensitivity analyses presented roughly similar results globally. Furthermore, the impact of COVID-19 on cardio-related outcomes appeared to be more pronounced in inpatients than in outpatients. Conclusion(s): The 12-month risk of incidental cardiovascular diseases is substantially higher in the COVID-19 survivors than the non-COVID- 19 controls. Clinicians and patients with a history of COVID-19 should pay attention to their cardiovascular health in long term.
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Background: There are a number of emerging case reports of various autoimmune diseases occurring after Covid-19, yet there is still no large-scale population-based evidence to support this potential association. This study provides a closer insight into the association between Covid-19 and autoimmune diseases and reveals discrepancies across sex, age and race of participants. Method(s): This is a retrospective cohort study based on the TriNetX US Collaborative Network. In the test-negative design, cases (N = 1,118,682) were participants with positive polymerase chain reaction (PCR) test results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV- 2), while controls were participants who tested negative and were not diagnosed Covid-19 throughout the follow-up period. The primary end points are incidence of newly-recorded autoimmune diseases. Hazard ratios (HRs) and 95% CIs of autoimmune diseases were calculated between propensity score-matched groups. We set osteoporosis as a negative control outcome to examine possible unmeasured confounders. We used another TriNetX database called Global Network for cross-validation to mitigate regional bias. Result(s): After propensity score matched for age, sex, race, socioeconomic status, and lifestyles variables, Covid-19 group exhibited significant higher risk of rheumatoid arthritis (HR:1.190, 95% CI:1.144-1.237), polymyalgia rheumatic (HR:1.147, 95% CI:1.016-1.296), vasculitis (HR:1.524, 95% CI:1.407-1.651), psoriasis (HR:1.264, 95% CI:1.203-1.329), and type 1 diabetes (HR:1.144, 95%CI:1.100-1.190), whereas it was associated with lower risk of systemic sclerosis (HR:0.767, 95% CI:0.668-0.881) and inflammatory bowel disease (HR:0.914, 95% CI:0.890-0.939). In general, age, sex, race and sensitivity analysis showed consistent trends in all strata. Conclusion(s): Covid-19 appears to be associated with a different degree of risk for various autoimmune diseases. Our preliminary findings have implications for clinical services and further research for mechanism is mandatory.
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The current study intends to identify the behavioural antecedents of investors' attitude and investment intention toward mutual funds using a robust SEM‐ANN approach. It focuses on novel factors in the purview of the COVID‐19 pandemic, increasing digitalization and social media usage. The research outcome indicates that attitude (ATB), awareness (AW) and investment decision involvement (IDI) have a significant positive relation with investment intention (BI). In contrast, perceived barrier (PBR) negatively relates to investment intention. Herd behaviour (HB) and social media influence (SMI) do not influence investment intention toward mutual funds. Moreover, all the tested predictors share direct relation with the attitude toward mutual fund investment, barring perceived risk (PR), which has an inverse relationship. As per the outcome of ANN sensitivity analysis, attitude is the most crucial determinant of investment intention. It is followed by awareness (AW), perceived barriers (PBR) and investment decision involvement (IDI). Among the significant determinants of attitude, self‐efficacy (SE) is the most important determinant, followed by perceived usefulness (PU), perceived emergency (PEMER), subjective norms (SN) and perceived risk (PR).
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The most reported symptoms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were initially fever, dry cough, and sore throat. However, as we continue to review the literature, the loss of taste and smell were also added as clinical symptoms of the novel SARS-CoV-2. At present, the effects of SARS-CoV-2 on the auditory system is still not well-understood. This study is mini-review and aims to find out more about the relationship between SARS-CoV-2 and hearing loss through review of the literature. From our findings, hearing loss is the primary otological symptom of SARS-CoV-2, followed by tinnitus and dizziness. In conclusion, SARS-CoV-2 may have an effect on our auditory system, but due to the small sample sizes in the existing literature, further prospective studies are warranted to determine the relationship between the virus and hearing loss. Copyright © The Author(s) 2022.
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Background/Purpose: Multisystem inflammatory syndrome associated with COVID-19 in children (MIS-C) is a rare but severe disease associated with coronavirus infection, in which various systems and organs are affected, including the heart, lungs, kidneys, brain, skin, eyes and gastrointestinal tract. One of the most severe features of this disease can be hemophagocytosis. The aim of this study is to assess the features of hemophagocytosis in MIS-C. Method(s): The retrospective study included 166 children (99 male, 67 female), aged from 4 months to 17 years (median 8.2 years), who met the WHO criteria for MIS-C. The analysis of the obtained data was performed using the STATISTICA software package, version 10.0 (StatSoft Inc., USA). Result(s): To study the signs of hemophagocytosis in patients with MIS-C they were divided into 2 equal groups: with HScore<=91 (n = 79) and with a HScore value >91 (n = 79). This division was done, since this value was associated with the severe life-threatening course of MIS-C and need in ICU admission (70.9% vs. 32.3%, P = 0.000002). Patients with HScore > 91 were more likely to have symptoms such as cervical lymphadenopathy (80.6% vs 54.1%, P = 0.0007), red dry cracked lips (63% vs 34.3%, P = 0.0007), face swelling (66.7% vs 34.7%, P = 0.001), hepatomegaly (84.2% vs 43.1%, P = 0.000000), splenomegaly (54.7% vs 43.1%, P = 0.0003), hypotension/shock (63.3% vs 25.3%, P = 0.000002), had higher levels of ESR (47 mm/h vs 34 mm/h, P = 0.0001), CRP (175.5 mg/L vs 125.8 mg/L, P = 0.01), D-dimer (2135 ng/mL vs. 1079 ng/mL, P = 0.0003), but lower levels of fibrinogen (3.1 g/L vs 5.6 g/L, P = 0.000002) erythrocytes (3.6 x 1012/L vs 4.0 x 1012/L, P = 0.000005), hemoglobin (98 g/L vs 112 g/L, P = 0.000000), and a tendency to thrombocytopenia (110 x 109/l vs 192 x 109/L, P = 0.0002) in 63.3% of patients. According to EchoCG data, signs of myocardial (45.5% vs 15.6%, P = 0.00006) and pericardial (45.5% vs 14.3%, P = 0.00002) lesions were more common in patients with HScore > 91. Patients with HScore > 91 more often needed treatment with IVIG (66.2% vs 24%, P = 0.000000), acetylsalicylic acid (65.7% vs. 47.1%, P = 0.027) and biological drugs (9.1% vs. 1.6%, P = 0.061). The average duration of hospitalization was also much longer in patients with HScore > 91 (23 days vs 14 days, P = 0.000000). Also, the identification of clinical and laboratory signs that were more common in the group of patients with HScore > 91 was performed using sensitivity and specificity analysis, and calculation of odds ratio. Results are presented in Table 1. Conclusion(s): Hemophagocytic syndrome is one of the most severe manifestations of MIS-C occuring in 35.4% of patients. It was found that HScore > 91 is associated with such a severe signs of MIS-C as myocarditis, pericarditis, hypotension/shock, and ICU admission. HScore is a simple tool that can also be used to assess the severity of MIS-C and dynamic monitoring.
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Many scholars have been challenged by multi-attribute group decision-making problems that have stimulated the appearance of increasingly general models. Pythagorean fuzzy sets were a reaction by Yager who in 2013, suggested this model to improve the performance of intuitionistic fuzzy sets. Another hybrid model –soft expert sets– deals with uncertain parameterized information. It considers opinions of different experts, improving the single-agent experience of soft sets. N-soft expert sets and their fuzzy version, namely, fuzzy N-soft expert sets, consider the ratings given to objects by more than one expert with respect to relevant parameters. The arguments supporting the need for independent allocation of membership and non-membership degrees apply to the fuzzy expressions imposed on top of the benefits of the N-soft expert environment. These challenges converge on the formulation of a new hybrid model called Pythagorean fuzzy N-soft expert sets that improves upon Pythagorean fuzzy sets with the benefits of N-soft expert sets. We study their scope of application with practical examples. Afterwards we discuss certain basic operators (subsethood, complement, union and intersection), prove some of their remarkable properties, and provide the concepts of equal, agree, and disagree-Pythagorean fuzzy N-soft expert sets. We present an algorithm for group decision-making problems in this framework and we explore three applications of this methodology, namely, to the analysis of wheat varieties, employee selection, and recovery order of patients suffering COVID-19. In the end, we provide a sensitivity analysis comparing the proposed model with some existing models to guarantee its cogency and feasibility. © 2023 The Author(s)