FaceTouch: Detecting hand-to-face touch with supervised contrastive learning to assist in tracing infectious disease (preprint)

Through our respiratory system, many viruses and diseases frequently spread and pass from one person to another. Covid-19 served as an example of how crucial it is to track down and cut back on contacts to stop its spread. There is a clear gap in finding automatic methods that can detect hand-to-face contact in complex urban scenes or indoors. In this paper, we introduce a computer vision framework, called FaceTouch, based on deep learning. It comprises deep sub-models to detect humans and analyse their actions. FaceTouch seeks to detect hand-to-face touches in the wild, such as through video chats, bus footage, or CCTV feeds. Despite partial occlusion of faces, the introduced system learns to detect face touches from the RGB representation of a given scene by utilising the representation of the body gestures such as arm movement. This has been demonstrated to be useful in complex urban scenarios beyond simply identifying hand movement and its closeness to faces. Relying on Supervised Contrastive Learning, the introduced model is trained on our collected dataset, given the absence of other benchmark datasets. The framework shows a strong validation in unseen datasets which opens the door for potential deployment.

The Use of Metaverse in the Healthcare Sector: Analysis and Applications

With the use of blockchain, Internet of Things, virtual platform/telecommunications network, artificial intelligence and the fourth industrial revolution, the essential demand for digital transition within the health care settings has increased as an outcome of the 2019 coronavirus illness outbreak and the fourth industrial revolution. The evolution of virtual environments with three-dimensional (3D) spaces and avatars, known as metaverse, has slowly gained acceptance in the field of health care. These environments may be especially useful for patient-facing platforms (such as platforms for telemedicine), functional uses (such as meeting management), digital education (such as modeled medical and surgical learning), treatments and diagnoses. This chapter offers the most recent state-of-the-art metaverse services and applications and a growing problem when it comes to using it in the healthcare sector. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

COVID-19 Severity Shifts the Cytokine Milieu Toward a Proinflammatory State in Egyptian Patients: A Cross-Sectional Study.

Despite extensive research to decipher the immunological basis of coronavirus disease (COVID-19), limited evidence on immunological correlates of COVID-19 severity from MENA region and Egypt was reported. In a single-center cross-sectional study, we have analyzed 25 cytokines that are related to immunopathologic lung injury, cytokine storm, and coagulopathy in plasma samples from 78 hospitalized Egyptian COVID-19 patients in Tanta University Quarantine Hospital and 21 healthy control volunteers between April 2020 and September 2020. The enrolled patients were divided into 4 categories based on disease severity, namely mild, moderate, severe, and critically ill. Interestingly, interleukin (IL)-1-α, IL-2Rα, IL-6, IL-8, IL-18, tumor necrosis factor-alpha (TNF-α), FGF1, CCL2, and CXC10 levels were significantly altered in severe and/or critically ill patients. Moreover, principal component analysis (PCA) demonstrated that severe and critically ill COVID-19 patients cluster based on specific cytokine signatures that distinguish them from mild and moderate COVID-19 patients. Specifically, levels of IL-2Rα, IL-6, IL-10, IL-18, TNF-α, FGF1, and CXCL10 largely contribute to the observed differences between early and late stages of COVID-19 disease. Our PCA showed that the described immunological markers positively correlate with high D-dimer and C-reactive protein levels and inversely correlate with lymphocyte counts in severe and critically ill patients. These data suggest a disordered immune regulation, particularly in severe and critically ill Egyptian COVID-19 patients, manifested as overactivated innate immune and dysregulated T-helper1 responses. Additionally, our study emphasizes the importance of cytokine profiling to identify potentially predictive immunological signatures of COVID-19 disease severity.

The impact of lncRNA-GAS5/miRNA-200/ACE2 molecular pathway on the severity of COVID-19.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is responsible for coronavirus disease (COVID-19), potentially has severe adverse effects, leading to public health crises worldwide. In COVID-19, deficiency of ACE-2 is linked to increased inflammation and cytokine storms via increased angiotensin II levels and decreased ACE-2/Mas receptor axis activity. MiRNAs are small sequences of noncoding RNAs that regulate gene expression by binding to the targeted mRNAs. MiR-200 dysfunction has been linked to the development of ARDS following acute lung injury and has been proposed as a key regulator of ACE2 expression. LncRNA growth arrest-specific transcript 5 (GAS5) has been recently studied for its modulatory effect on the miRNA-200/ACE2 axis. OBJECTIVE: The current study aims to investigate the role of lncRNA GAS5, miRNA-200, and ACE2 as new COVID-19 diagnostic markers capable of predicting the severity of SARS-CoV-2 complications. METHODS: A total of 280 subjects were classified into three groups: COVID-19-negative controls (n=80), and COVID-19 patients (n=200) who required hospitalization were classified into two groups: group (2) moderate cases (n=112) and group (3) severe cases (n = 88). RESULTS: The results showed that the serum GAS5 expression was significantly down-expressed in COVID-19 patients; as a consequence, the expression of miR-200 was reported to be overexpressed and its targeted ACE2 was down-regulated. The ROC curve was drawn to examine the diagnostic abilities of GAS5, miR-200, and ACE2, yielding high diagnostic accuracy with high sensitivity and specificity. CONCLUSION: lncRNA-GAS5, miRNA-200, and ACE2 panels presented great diagnostic potential as they demonstrated the highest diagnostic accuracy for discriminating moderate COVID-19 and severe COVID-19 cases.

Simultaneous Super-Resolution and Classification of Lung Disease Scans.

Acute lower respiratory infection is a leading cause of death in developing countries. Hence, progress has been made for early detection and treatment. There is still a need for improved diagnostic and therapeutic strategies, particularly in resource-limited settings. Chest X-ray and computed tomography (CT) have the potential to serve as effective screening tools for lower respiratory infections, but the use of artificial intelligence (AI) in these areas is limited. To address this gap, we present a computer-aided diagnostic system for chest X-ray and CT images of several common pulmonary diseases, including COVID-19, viral pneumonia, bacterial pneumonia, tuberculosis, lung opacity, and various types of carcinoma. The proposed system depends on super-resolution (SR) techniques to enhance image details. Deep learning (DL) techniques are used for both SR reconstruction and classification, with the InceptionResNetv2 model used as a feature extractor in conjunction with a multi-class support vector machine (MCSVM) classifier. In this paper, we compare the proposed model performance to those of other classification models, such as Resnet101 and Inceptionv3, and evaluate the effectiveness of using both softmax and MCSVM classifiers. The proposed system was tested on three publicly available datasets of CT and X-ray images and it achieved a classification accuracy of 98.028% using a combination of SR and InceptionResNetv2. Overall, our system has the potential to serve as a valuable screening tool for lower respiratory disorders and assist clinicians in interpreting chest X-ray and CT images. In resource-limited settings, it can also provide a valuable diagnostic support.

Vitamin D deficiency and vitamin D receptor FokI polymorphism as risk factors for COVID-19.

BACKGROUND: Given the sparse data on vitamin D status in pediatric COVID-19, we investigated whether vitamin D deficiency could be a risk factor for susceptibility to COVID-19 in Egyptian children and adolescents. We also investigated whether vitamin D receptor (VDR) FokI polymorphism could be a genetic marker for COVID-19 susceptibility. METHODS: One hundred and eighty patients diagnosed to have COVID-19 and 200 matched control children and adolescents were recruited. Patients were laboratory confirmed as SARS-CoV-2 positive by real-time RT-PCR. All participants were genotyped for VDR Fok1 polymorphism by RT-PCR. Vitamin D status was defined as sufficient for serum 25(OH) D at least 30 ng/mL, insufficient at 21-29 ng/mL, deficient at <20 ng/mL. RESULTS: Ninety-four patients (52%) had low vitamin D levels with 74 (41%) being deficient and 20 (11%) had vitamin D insufficiency. Vitamin D deficiency was associated with 2.6-fold increased risk for COVID-19 (OR = 2.6; [95% CI 1.96-4.9]; P = 0.002. The FokI FF genotype was significantly more represented in patients compared to control group (OR = 4.05; [95% CI: 1.95-8.55]; P < 0.001). CONCLUSIONS: Vitamin D deficiency and VDR Fok I polymorphism may constitute independent risk factors for susceptibility to COVID-19 in Egyptian children and adolescents. IMPACT: Vitamin D deficiency could be a modifiable risk factor for COVID-19 in children and adolescents because of its immune-modulatory action. To our knowledge, ours is the first such study to investigate the VDR Fok I polymorphism in Caucasian children and adolescents with COVID-19. Vitamin D deficiency and the VDR Fok I polymorphism may constitute independent risk factors for susceptibility to COVID-19 in Egyptian children and adolescents. Clinical trials should be urgently conducted to test for causality and to evaluate the efficacy of vitamin D supplementation for prophylaxis and treatment of COVID-19 taking into account the VDR polymorphisms.

Statistical Inference of Truncated Cauchy Power-Inverted Topp–Leone Distribution under Hybrid Censored Scheme with Applications

In this article, a new two-parameter model called the truncated Cauchy power-inverted Topp–Leone (TCP-ITL) is constructed by merging the truncated Cauchy power -G (TCP-G) family with the inverted Topp–Leone (ITL) distribution. Some structural properties of the newly suggested model are obtained. Different types of entropies are proposed under the TCP-ITL distribution. Under the complete and hybrid censored data, the maximum likelihood (ML), maximum product of spacing (MPSP), and Bayesian estimate approaches are explored. A simulation study is developed to test the proposed distribution's restricted sample attributes. In the majority of cases, the numerical data revealed that the Bayesian estimates provided more accurate outcomes than the equivalent alternative estimates. The adaptability of the proposed approach is proven using examples from dependability, medicine, and engineering. A real-world data set is utilized to demonstrate the potential of the TCP-ITL distribution in comparison to other well-known distributions. The results of the model selection revealed that the proposed distribution is the best choice for the data sets under consideration. © 2023 by the authors.

Phenylpyrazolone-1,2,3-triazole Hybrids as Potent Antiviral Agents with Promising SARS-CoV-2 Main Protease Inhibition Potential.

COVID-19 infection is now considered one of the leading causes of human death. As an attempt towards the discovery of novel medications for the COVID-19 pandemic, nineteen novel compounds containing 1,2,3-triazole side chains linked to phenylpyrazolone scaffold and terminal lipophilic aryl parts with prominent substituent functionalities were designed and synthesized via a click reaction based on our previous work. The novel compounds were assessed using an in vitro effect on the growth of SARS-CoV-2 virus-infested Vero cells with different compound concentrations: 1 and 10 µM. The data revealed that most of these derivatives showed potent cellular anti-COVID-19 activity and inhibited viral replication by more than 50% with no or weak cytotoxic effect on harboring cells. In addition, in vitro assay employing the SARS-CoV-2-Main protease inhibition assay was done to test the inhibitors' ability to block the common primary protease of the SARS-CoV-2 virus as a mode of action. The obtained results show that the one non-linker analog 6h and two amide-based linkers 6i and 6q were the most active compounds with IC50 values of 5.08, 3.16, and 7.55 µM, respectively, against the viral protease in comparison to data of the selective antiviral agent GC-376. Molecular modeling studies were done for compound placement within the binding pocket of protease which reveal conserved residues hydrogen bonding and non-hydrogen interactions of 6i analog fragments: triazole scaffold, aryl part, and linker. Moreover, the stability of compounds and their interactions with the target pocket were also studied and analyzed by molecular dynamic simulations. The physicochemical and toxicity profiles were predicted, and the results show that compounds behave as an antiviral activity with low or no cellular or organ toxicity. All research results point to the potential usage of new chemotype potent derivatives as promising leads to be explored in vivo that might open the door to rational drug development of SARS-CoV-2 Main protease potent medicines.

A computational supervised neural network procedure for the fractional SIQ mathematical model.

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. Supplementary Information: The online version contains supplementary material available at 10.1140/epjs/s11734-022-00738-9.

Impact of COVID - 19 Pandemic on Teaching Strategy methods among Students in higher education Institutions in Jeddah City

During the COVID-19 pandemic, most learning strategies have been transitioned to an online setting across the world. Students and teachers who previously relied on traditional learning are now confronted with a new challenge. This dramatic adjustment may have an impact on their teaching strategy methods, learning habits, and willingness to embrace the change. A descriptive cross sectional online survey was used among students in selected higher education institutes in Jeddah city. The intended aim of this study was to assess the impact of COVID-19 pandemic on selecting teaching strategy methods and to measure the students' perceptions. Using the Non-Probability Snowball Sample technique, 220 student participants were chosen, and the results were then analyzed using the SPSS program. According to the students' results of the survey, although the participant faces difficulties from the virtual transition classes (42.7%), the teaching strategy in online transition seems to be more beneficial in the future and it will continue after COVID- 19 with (65.5%) of student's approval. Overall, the modern teaching strategy methods that have been measured on students in Jeddah city demonstrated a highly positive impact in higher education institutions. Keywords: Assess the impact of COVID-19 pandemic, teaching strategy methods, virtual transition classes, students' results of the survey.

The Health Impacts of Social Distancing Among Indigenous People in Ontario During the First Wave of COVID-19

Among Indigenous Peoples in Canada and around the world, the health impacts of COVID-19 have been measured largely through biological, social, and psychological impacts. Our study draws from a relational concept of health to examine two objectives: (1) how social distancing protocols have shaped Indigenous connections with self, family, wider community, and nature;and (2) what these changing relationships mean for perceptions of Indigenous health. Carried out by an Indigenous team of scholars, community activists, and students, this research draws from a decolonizing methodology and qualitative interviews (n = 16) with Indigenous health and social care providers in urban and reserve settings. Our results illustrate a considerable decline in interpersonal connections such as with family, community organizations, and larger social networks as a result of social distancing. Among those already vulnerable, underlying health, social, and economic inequities have been exacerbated. While the health impacts of COVID-19 have been overwhelmingly negative, participants noted the importance of this time for self-reflection and reconnection of human-kind with Mother Earth. This paper offers an alternative perspective to popularized views of Indigenous experiences of COVID-19 as they relate to vulnerability and resilience.

Use of Quantitative Faecal Immunochemical Test (Qfit) as a Prioritization Tool in Investigation of Colorectal-Cancer Suspected Referrals during Covid-19 Pandemic in Nhs Ayrshire and Arran Hospitals

Aim: British Society of Gastroenterology advised reduced level of endoscopic services due to COVID-19 pandemic. This resulted in a significant rise in number of cancer suspected patients waiting for colonoscopy. Quantitative Faecal Immunochemical Test(QFIT)is designed to detect occult blood in stool(0 to 400 ug/g). The value of QFIT is proportional to cancer risk. Therefore, it can be utilised to prioritize cancer suspected referrals prior to colonoscopy. In this audit of service we investigated:1)The reliability of QFIT as prioritization tool for colorectal cancer referrals.2)To investigate the impact of QFIT on colonoscopy burden. Method(s): QFIT was applied to all colorectal cancer suspected referrals to NHS Ayrshire & Arran in September 2020. An interim audit of the outcome was performed focusing on four months' worth of data. QFIT values recorded and subsequent management steps analysed in following categories. QFIT less than 10,10 to 400, and over 400. The highest risk of cancer is within over 400. Result(s): 1258 test kits were dispatched,823(65%)results obtained. QFIT values were as following:631 samples with < 10(77%)of returned samples;of these 490 patients were discharged,27 patients' tests was repeated, and 114 patients were planned for further endoscopic or imaging investigations.143 patients had levels between 10 to 400. The main finding was that 49(6%)patients had QFIT more than 400, and were booked for colonoscopy. Nine patients had a confirmed cancer diagnosis. Conclusion(s): In our cohort,9 patients with QFIT more than 400 were diagnosed with cancer at colonoscopy. This is in concordance with published data reporting similer values. This would re-affirm reliability of QFIT as a prioritisation tool in predicting colorectal cancer. Majority of QFIT less than 10 were discharged following clinic consultation. This resulted in significant reduction of colonoscopy numbers that would have otherwise been performed. Longer term review of all QFIT categories is necessary to advise on its usefulness, out width the pandemic.

A computational supervised neural network procedure for the fractional SIQ mathematical model

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. Supplementary Information The online version contains supplementary material available at 10.1140/epjs/s11734-022-00738-9.

Validated HPLC-PDA methodology utilized for simultaneous determination of Etoricoxib and Paracetamol in the presence of Paracetamol toxic impurities.

Etoricoxib (ETO), Paracetamol (PCM), and two toxic impurities for Paracetamol impurity K (4-aminophenol (PAP)) and impurity E (para-hydroxy acetophenone (PHA)) were separated using a simple and selective HPLC method that was tested for the first time. PCM is a commonly used analgesic and antipyretic medication that has recently been incorporated into COVID-19 supportive treatment. Pharmaceuticals containing PCM in combination with other analgesic-antipyretic drugs like ETO help to improve patient compliance. The studied drugs and impurities were separated on a GL Sciences Inertsil ODS-3 (250 × 4.6) mm, 5.0 µm column, and linear gradient elution was performed using 50 mM potassium dihydrogen phosphate adjusted to pH 4.0 with ortho-phosphoric acid and acetonitrile as mobile phase at 2.0 mL/min flow rate at 25 °C and UV detection at 220 nm. The linearity range was 1.5-30.0 µg/mL for ETO and PCM while 0.5-10.0 µg/mL for PAP and PHA, with correlation coefficients (r) for ETO, PCM, PAP, and PHA of 0.9999, 0.9993, 0.9996, and 0.9998, respectively. The proposed method could be used well for routine analysis in quality control laboratory.

The Effect of the Aesthetically Mobile Interfaces on Students’ Learning Experience for Primary Education

Mobile devices such as mobile phones are becoming more important to school students today. This is due to the COVID-19 pandemic, mostly traditional face-to-face learning has shifted to online learning such as learning via a mobile platform. Mobile learning also known as m-learning, is defined as learning in numerous situations through social and content interaction utilizing personal electronic devices. M-learning applications not only need to have efficient functions, but it also has to attract students to learn by providing an attractive interface. An aesthetic of a mobile interface is essential since it could influence the user's learning experiences, but vice versa for non-aesthetic interfaces. User experience (UX) encompasses an extensive range of outcomes of the user-device interaction, including cognitions, attitudes, beliefs, behaviour, behavioural intentions, and affect. However, this study focuses on UX in terms of learnability, satisfaction, and efficiency since most previous studies were not explicitly focused on examining these three (3) UX components. Thus, this study aims to investigate the effect of aesthetically mobile interfaces on the learnability, satisfaction, and efficiency of primary school students, specifically, for Kelas Al-Quran and Fardu Ain (KAFA) students. This study found that aesthetically mobile interfaces significantly affected students’ learning experiences regarding learnability, satisfaction, and efficiency. In conclusion, the findings of this study could serve as guidelines for future research in the field of mobile interface design. © 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved. All Rights Reserved.

A Comprehensive In Silico Study of New Metabolites from Heteroxenia fuscescens with SARS-CoV-2 Inhibitory Activity.

Chemical investigation of the total extract of the Egyptian soft coral Heteroxenia fuscescens, led to the isolation of eight compounds, including two new metabolites, sesquiterpene fusceterpene A (1) and a sterol fuscesterol A (4), along with six known compounds. The structures of 1-8 were elucidated via intensive studies of their 1D, 2D-NMR, and HR-MS analyses, as well as a comparison of their spectral data with those mentioned in the literature. Subsequent comprehensive in-silico-based investigations against almost all viral proteins, including those of the new variants, e.g., Omicron, revealed the most probable target for these isolated compounds, which was found to be Mpro. Additionally, the dynamic modes of interaction of the putatively active compounds were highlighted, depending on 50-ns-long MDS. In conclusion, the structural information provided in the current investigation highlights the antiviral potential of H. fuscescens metabolites with 3ß,5α,6ß-trihydroxy steroids with different nuclei against SARS-CoV-2, including newly widespread variants.

A mathematical model of coronavirus transmission by using the heuristic computing neural networks.

In this study, the nonlinear mathematical model of COVID-19 is investigated by stochastic solver using the scaled conjugate gradient neural networks (SCGNNs). The nonlinear mathematical model of COVID-19 is represented by coupled system of ordinary differential equations and is studied for three different cases of initial conditions with suitable parametric values. This model is studied subject to seven class of human population N(t) and individuals are categorized as: susceptible S(t), exposed E(t), quarantined Q(t), asymptotically diseased IA (t), symptomatic diseased IS (t) and finally the persons removed from COVID-19 and are denoted by R(t). The stochastic numerical computing SCGNNs approach will be used to examine the numerical performance of nonlinear mathematical model of COVID-19. The stochastic SCGNNs approach is based on three factors by using procedure of verification, sample statistics, testing and training. For this purpose, large portion of data is considered, i.e., 70%, 16%, 14% for training, testing and validation, respectively. The efficiency, reliability and authenticity of stochastic numerical SCGNNs approach are analysed graphically in terms of error histograms, mean square error, correlation, regression and finally further endorsed by graphical illustrations for absolute errors in the range of 10-05 to 10-07 for each scenario of the system model.

Swarming morlet wavelet neural network procedures for the mathematical robot system.

The task of this work is to present the solutions of the mathematical robot system (MRS) to examine the positive coronavirus cases through the artificial intelligence (AI) based Morlet wavelet neural network (MWNN). The MRS is divided into two classes, infected I ( θ ) and Robots R ( θ ) . The design of the fitness function is presented by using the differential MRS and then optimized by the hybrid of the global swarming computational particle swarm optimization (PSO) and local active set procedure (ASP). For the exactness of the AI based MWNN-PSOIPS, the comparison of the results is presented by using the proposed and reference solutions. The reliability of the MWNN-PSOASP is authenticated by extending the data into 20 trials to check the performance of the scheme by using the statistical operators with 10 hidden numbers of neurons to solve the MRS.

Artificial neural network-based heuristic to solve COVID-19 model including government strategies and individual responses.

The current work aims to design a computational framework based on artificial neural networks (ANNs) and the optimization procedures of global and local search approach to solve the nonlinear dynamics of the spread of COVID-19, i.e., the SEIR-NDC model. The combination of the Genetic algorithm (GA) and active-set approach (ASA), i.e., GA-ASA, works as a global-local search scheme to solve the SEIR-NDC model. An error-based fitness function is optimized through the hybrid combination of the GA-ASA by using the differential SEIR-NDC model and its initial conditions. The numerical performances of the SEIR-NDC nonlinear model are presented through the procedures of ANNs along with GA-ASA by taking ten neurons. The correctness of the designed scheme is observed by comparing the obtained results based on the SEIR-NDC model and the reference Adams method. The absolute error performances are performed in suitable ranges for each dynamic of the SEIR-NDC model. The statistical analysis is provided to authenticate the reliability of the proposed scheme. Moreover, performance indices graphs and convergence measures are provided to authenticate the exactness and constancy of the proposed stochastic scheme.