The impact of vaccination on the dynamics of the Omicron variant of SARS-CoV-2

In the context of vaccination, we develop a novel mathematical model to examine the Omicron type of coronavirus illness. The system's mathematical analysis based on its equilibrium points shall be obtained. The threshold quantity is used to investigate the system's local and global asymptotical analysis. The Omicron vaccination model shown to be stable locally asymptotically if R 0 v < 1 . The system is globally asymptotically stable at the disease-free equilibrium for a special case when η = 1 if R 0 v < 1 . We estimate the model parameters based on the observed data and show that the threshold is R 0 ≈ 2.4894 in the absence of vaccination. The model has the phenomenon of backward bifurcation under certain conditions. Herd immunity analysis is obtained and it turns out that the herd immunity threshold for the South African population is 74%. The impact of vaccination on disease dynamics is also shown and discussed. Further, we have given some graphical results showing the community's disease reduction. [ FROM AUTHOR] Copyright of Waves in Random & Complex Media is the property of Taylor & Francis Ltd 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.)

Exploring the inhibitory potential of novel piperidine-derivatives against main protease (Mpro) of SARS-CoV-2: A hybrid approach consisting of molecular docking, MD simulations and MMPBSA analysis

Graphical In the current study, a hybrid computational approach consisting of different computational methods to explore the molecular electronic structures, bioactivity and therapeutic potential of piperidine compounds against SARS-CoV-2. The quantum chemical methods are used to study electronic structures of designed derivatives, molecular docking methods are used to see the most potential docking interactions for main protease (MPro) of SARS-CoV-2 while molecular dynamic and MMPBSA analyses are performed in bulk water solvation process to mimic real protein like aqueous environment and effectiveness of docked complexes. We designed and optimized piperidine derivatives from experimentally known precursor using quantum chemical methods. The UV-Visible, IR, molecular orbitals, molecular electrostatic plots, and global chemical reactivity descriptors are carried out which illustrate that the designed compounds are kinetically stable and reactive. The results of MD simulations and binding free energy revealed that all the complex systems possess adequate dynamic stability, and flexibility based on their RMSD, RMSF, radius of gyration, and hydrogen bond analysis. The computed net binding free energy

Effects of COVID-19 on Synaptic and Neuronal Degeneration.

Neurons are the basic building blocks of the human body's neurological system. Atrophy is defined by the disintegration of the connections between cells that enable them to communicate. Peripheral neuropathy and demyelinating disorders, as well as cerebrovascular illnesses and central nervous system (CNS) inflammatory diseases, have all been linked to brain damage, including Parkinson's disease (PD). It turns out that these diseases have a direct impact on brain atrophy. However, it may take some time after the onset of one of these diseases for this atrophy to be clearly diagnosed. With the emergence of the Coronavirus disease 2019 (COVID-19) pandemic, there were several clinical observations of COVID-19 patients. Among those observations is that the virus can cause any of the diseases that can lead to brain atrophy. Here we shed light on the research that tracked the relationship of these diseases to the COVID-19 virus. The importance of this review is that it is the first to link the relationship between the Coronavirus and diseases that cause brain atrophy. It also indicates the indirect role of the virus in dystrophy.

A Mathematical Model of Vaccinations Using New Fractional Order Derivative.

Purpose: This paper studies a simple SVIR (susceptible, vaccinated, infected, recovered) type of model to investigate the coronavirus's dynamics in Saudi Arabia with the recent cases of the coronavirus. Our purpose is to investigate coronavirus cases in Saudi Arabia and to predict the early eliminations as well as future case predictions. The impact of vaccinations on COVID-19 is also analyzed. Methods: We consider the recently introduced fractional derivative known as the generalized Hattaf fractional derivative to extend our COVID-19 model. To obtain the fitted and estimated values of the parameters, we consider the nonlinear least square fitting method. We present the numerical scheme using the newly introduced fractional operator for the graphical solution of the generalized fractional differential equation in the sense of the Hattaf fractional derivative. Mathematical as well as numerical aspects of the model are investigated. Results: The local stability of the model at disease-free equilibrium is shown. Further, we consider real cases from Saudi Arabia since 1 May−4 August 2022, to parameterize the model and obtain the basic reproduction number R0v≈2.92. Further, we find the equilibrium point of the endemic state and observe the possibility of the backward bifurcation for the model and present their results. We present the global stability of the model at the endemic case, which we found to be globally asymptotically stable when R0v>1. Conclusion: The simulation results using the recently introduced scheme are obtained and discussed in detail. We present graphical results with different fractional orders and found that when the order is decreased, the number of cases decreases. The sensitive parameters indicate that future infected cases decrease faster if face masks, social distancing, vaccination, etc., are effective.

A Novel Multicolor-thresholding Auto-detection Method to Detect the Location and Severity of Inflammation in Confirmed SARS-COV-2 Cases using Chest X-Ray Images.

OBJECTIVES: Since late 2019, Coronavirus Disease 2019 (COVID-19) has spread around the world. It has been determined that the disease is very contagious and can cause Acute Respiratory Distress (ARD). Medical imaging has the potential to help identify, detect, and quantify the severity of this infection. This work seeks to develop a novel auto-detection technique for verified COVID-19 cases that can detect aberrant alterations in traditional X-ray pictures. METHODS: Nineteen separately colored layers were created from X-ray scans of patients diagnosed with COVID-19. Each layer represents objects that have a similar contrast and can be represented by a single color. In a single layer, objects with similar contrasts are formed. A single color image was created by extracting all the objects from all the layers. The prototype model could recognize a wide range of abnormal changes in the image texture based on color differentiation. This was true even when the contrast values of the detected unclear abnormalities varied slightly. RESULTS: The results indicate that the proposed novel method is 91% accurate in detecting and grading COVID-19 lung infections compared to the opinions of three experienced radiologists evaluating chest X-ray images. Additionally, the method can be used to determine the infection site and severity of the disease by categorizing X-rays into five severity levels. CONCLUSION: By comparing affected tissue to healthy tissue, the proposed COVID-19 auto-detection method can identify locations and indicate the severity of the disease, as well as predict where the disease may spread.

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure.

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic's short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic's aftereffects, utilising the notion of the bullwhip phenomenon's short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.

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.

Association of disease severity and death outcome with vaccination status of admitted COVID-19 patients in delta period of SARS-COV-2 in mixed variety of vaccine background.

To understand the effectual role of COVID-19 vaccination, we must analyze its effectiveness in dampening the disease severity and death outcome in patients who acquire infection and require hospitalization. The goal of this study was to see if there was an association between disease progression in admitted COVID-19 patients and their prior vaccination exposure. A prospective cohort study based on 1640 admitted COVID-19 patients were carried between June 2021 and October 2021. Depending on vaccination exposure they were divided into vaccinated (exposed) and unvaccinated (unexposed) groups, excluding partially vaccinated patients. Disease severity was assessed at admission on severity index scale. Disease progression to mortality or need of mechanical ventilation and survival were taken as outcome. Absolute difference with 95%CI and Risk Ratio were calculated using cross tabulation, Chi square test and multivariable logistic regression analysis. Among 1514 total analyzed cohort (median age, 53 years [IQR, 17,106]; 43.7% from 46 to 65 years of age group, 56.2% males,33.4% with no comorbid factor for disease progression) 369(24.4%) were vaccinated breakthrough cases and 1145(75.6%) were unvaccinated controls. 556(36.7%) progressed to death or mechanical ventilation, 958(63.3%) patients survived and were discharged home. Disease progression to death or mechanical ventilation was significantly associated with decreased likelihood of vaccination (24.9% among vaccinated breakthrough vs 40.5% unvaccinated controls, [Absolute difference -15.6% 95%CI (-10.2% to -20.6%); RR 0.615 95%CI (0.509, 0.744); p <.001]). This association was stronger for old age population and for increase time span between second dose of vaccine and onset of symptoms. There was no statistically significant difference among different types of vaccination and occurrence of outcome when compared to unvaccinated controls (RR 0.607(0.482, 0.763); 0.673(0.339, 1.33) and 0.623(0.441, 0.881) for Inactivated virus vaccine, mRNA and Adenovirus vector-based vaccine respectively. The patients who were fully vaccinated against SARS-COV-2 die or shift to mechanical ventilation less frequently than unvaccinated COVID-19 admitted patients.

Insighting isatin derivatives as potential antiviral agents against NSP3 of COVID-19.

The world is now facing intolerable damage in all sectors of life because of the deadly COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus 2. The discovery and development of anti-SARS-CoV-2 drugs have become pragmatic in the time needed to fight against this pandemic. The non-structural protein 3 is essential for the replication of transcriptase complex (RTC) and may be regarded as a possible target against SARS-CoV-2. Here, we have used a comprehensive in silico technique to find potent drug molecules against the NSP3 receptor of SARS-CoV-2. Virtual screening of 150 Isatin derivatives taken from PubChem was performed based on their binding affinity estimated by docking simulations, resulting in the selection of 46 ligands having binding energy greater than -7.1 kcal/mol. Moreover, the molecular interactions of the nine best-docked ligands having a binding energy of ≥ -8.5 kcal/mol were analyzed. The molecular interactions showed that the three ligands (S5, S16, and S42) were stabilized by forming hydrogen bonds and other significant interactions. Molecular dynamic simulations were performed to mimic an in vitro protein-like aqueous environment and to check the stability of the best three ligands and NSP3 complexes in an aqueous environment. The binding energy of the S5, S16, and S42 systems obtained from the molecular mechanics Poisson-Boltzmann surface area also favor the system's stability. The MD and MM/PBSA results explore that S5, S16, and S42 are more stable and can be considered more potent drug candidates against COVID-19 disease. Supplementary Information: The online version contains supplementary material available at 10.1007/s11696-022-02298-7.

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.

Evaluation of the BioFire FilmArray Pneumonia Panel Plus to the Conventional Diagnostic Methods in Determining the Microbiological Etiology of Hospital-Acquired Pneumonia.

Hospital-acquired pneumonia (HAP) is a substantial public health issue that is associated with high mortality rates and is complicated by an arsenal of microbial etiologies, expressing multidrug-resistant phenotypes, rendering relatively limited therapeutic options. BioFire FilmArray Pneumonia Panel plus (BFPP) is a simple multiplexed PCR system that integrates sample preparation, nucleic acid extraction, amplification, and analysis of microbial etiology, with a turnaround time of about one hour. In comparison to standard culture methods, BFPP is simpler, easier to perform, and can simultaneously detect the most common pathogens involved in lower respiratory tract infections (34 targets). Accordingly, we evaluated the diagnostic performance of the multiplexed BFPP for the rapid detection of 27 clinically relevant respiratory pathogens and 7 genetic markers among 50 HAP cases admitted to the intensive care unit (ICU), who submitted mini-bronchoalveolar (mBAL) specimens. In comparison to standard culture methods, BFPP showed an overall sensitivity of 100% [95% CI; 90-100] and overall specificity of 90% [95% CI; 87.4-92.5] among all the tested bacterial targets. BFPP identified 11 viral targets (22%) among the tested specimens. The BFPP semi-quantitative analysis showed a concordance rate of 47.4% among positive culture specimens. For the investigation of the antibiotic resistance genes, BFPP showed a positive percent agreement (PPA), a negative percent agreement (NPA), and an overall percent agreement (OPA), reaching 97% [95% CI; 90-100], 95% [95% CI; 91.5-97], and 95% [95% CI; 93-97], respectively, with standard antibiotic sensitivity testing. In conclusion, BFPP has the potential to enhance the rapid microbiological diagnosis of HAP cases, and could aid in tailoring appropriate antibiotic therapies.

Lipopolysaccharide induces acute lung injury and alveolar haemorrhage in association with the cytokine storm, coagulopathy and AT1R/JAK/STAT augmentation in a rat model that mimics moderate and severe Covid-19 pathology.

Progress in the study of Covid-19 disease in rodents has been hampered by the lack of angiotensin-converting enzyme 2 (ACE2; virus entry route to the target cell) affinities for the virus spike proteins across species. Therefore, we sought to determine whether a modified protocol of lipopolysaccharide (LPS)-induced acute respiratory distress syndrome in rats can mimic both cell signalling pathways as well as severe disease phenotypes of Covid-19 disease. Rats were injected via intratracheal (IT) instillation with either 15 mg/kg of LPS (model group) or saline (control group) before being killed after 3 days. A severe acute respiratory syndrome (SARS)-like effect was observed in the model group as demonstrated by the development of a "cytokine storm" (>2.7 fold increase in blood levels of IL-6, IL-17A, GM-CSF, and TNF-α), high blood ferritin, demonstrable coagulopathy, including elevated D-dimer (approximately 10-fold increase), PAI-1, PT, and APTT (p < 0.0001). In addition, LPS increased the expression of lung angiotensin II type I receptor (AT1R)-JAK-STAT axis (>4 fold increase). Chest imaging revealed bilateral small patchy opacities of the lungs. Severe lung injury was noted by the presence of both, alveolar collapse and haemorrhage, desquamation of epithelial cells in the airway lumen, infiltration of inflammatory cells (CD45+ leukocytes), widespread thickening of the interalveolar septa, and ultrastructural alterations similar to Covid-19. Thus, these findings demonstrate that IT injection of 15 mg/kg LPS into rats, induced an AT1R/JAK/STAT-mediated cytokine storm with resultant pneumonia and coagulopathy that was commensurate with moderate and severe Covid-19 disease noted in humans.

A mathematical model for SARS-CoV-2 in variable-order fractional derivative.

A new coronavirus mathematical with hospitalization is considered with the consideration of the real cases from March 06, 2021 till the end of April 30, 2021. The essential mathematical results for the model are presented. We show the model stability when R 0 < 1 in the absence of infection. We show that the system is stable locally asymptotically when R 0 < 1 at infection free state. We also show that the system is globally asymptotically stable in the disease absence when R 0 < 1 . Data have been used to fit accurately to the model and found the estimated basic reproduction number to be R 0 = 1.2036 . Some graphical results for the effective parameters are drawn for the disease elimination. In addition, a variable-order model is introduced, and so as to handle the outbreak effectively and efficiently, a genetic algorithm is used to produce high-quality control. Numerical simulations clearly show that decision-makers may develop helpful and practical strategies to manage future waves by implementing optimum policies.

Mathematical modeling and stability analysis of the COVID-19 with quarantine and isolation.

The present paper focuses on the modeling of the COVID-19 infection with the use of hospitalization, isolation and quarantine. Initially, we construct the model by spliting the entire population into different groups. We then rigorously analyze the model by presenting the necessary basic mathematical features including the feasible region and positivity of the problem solution. Further, we evaluate the model possible equilibria. The theoretical expression of the most important mathematical quantity of major public health interest called the basic reproduction number is presented. We are taking into account to study the disease free equilibrium by studying its local and global asymptotical analysis. We considering the cases of the COVID-19 infection of Pakistan population and find the parameters using the estimation with the help of nonlinear least square and have R 0 ≈ 1 . 95 . Further, to determine the influence of the model parameters on disease dynamics we perform the sensitivity analysis. Simulations of the model are presented using estimated parameters and the impact of various non-pharmaceutical interventions on disease dynamics is shown with the help of graphical results. The graphical interpretation justify that the effective utilization of keeping the social-distancing, making the quarantine of people (or contact-tracing policy) and to make hospitalization of confirmed infected people that dramatically reduces the number of infected individuals (enhancing the quarantine or contact-tracing by 50% from its baseline reduces 84% in the predicted number of confirmed infected cases). Moreover, it is observed that without quarantine and hospitalization the scenario of the disease in Pakistan is very worse and the infected cases are raising rapidly. Therefore, the present study suggests that still, a proper and effective application of these non-pharmaceutical interventions are necessary to curtail or minimize the COVID-19 infection in Pakistan.

New insights on mucormycosis and its association with the COVID-19 pandemic.

COVID-19 continues to cause significant fatality worldwide. Glucocorticoids prove to play essential roles in COVID-19 management; however, the extensive use of steroids together with the virus immune dysregulation may increase the danger of secondary infections with mucormycosis, an angioinvasive fungal infection. Unfortunately, a definite correlation between COVID-19 and elevated mucormycosis infection cases is now clear worldwide. In this review, we discuss the historical record and epidemiology of mucormycosis as well as pathogenesis and associated host immune response, risk factors, clinical presentation, diagnosis and treatment. Special emphasis is given to its association with the current COVID-19 pandemic, including latest updates on COVID-19-associated mucormycosis cases globally, with recommendations for efficacious management.

Implementation of a Blockchain System Using Improved Elliptic Curve Cryptography Algorithm for the Performance Assessment of the Students in the E-Learning Platform

Blockchain technology allows for the decentralized creation of a propagated record of digital events, in which third parties do not control information and associated transactions. This methodology was initially developed for value transmission. Still, it now has a broad array of utilization in various industries, including health, banking, the internet of things, and several others. With its numerous added benefits, a blockchain-based learning management system is a commonly utilized methodology at academic institutes, and more specifically during and after the COVID-19 period. It also presents several potentials for decentralized, interoperable record management in the academic system in education. Integrity, authenticity, and peer-executed smart contracts (SC) are some of the qualities of a blockchain that could introduce a new degree of safety, trustworthiness, and openness to e-learning. This research proposes a unique encryption technique for implementing a blockchain system in an e-learning (EL) environment to promote transparency in assessment procedures. Our methodology may automate evaluations and provide credentials. We built it to be analytical and content-neutral in order to demonstrate the advantages of a blockchain back-end to end-users, including student and faculty members particularly during this COVID-19 era. This article explains the employment of blockchain and SC in e-learning. To improve the trust in the assessment, we propose a novel improved elliptic curve cryptography algorithm (IECCA) for data encryption and decryption. The performance of the suggested method is examined by comparing it with various existing algorithms of encryption. The evaluation of the behaviour of the presented method demonstrates that the technique shall enhance trust in online educational systems, assessment processes, educational history, and credentials.

Mathematical Analysis of the TB Model with Treatment via Caputo-Type Fractional Derivative

In this study, we formulate a noninteger-order mathematical model via the Caputo operator for the transmission dynamics of the bacterial disease tuberculosis (TB) in Khyber Pakhtunkhwa (KP), Pakistan. The number of confirmed cases from 2002 to 2017 is considered as incidence data for the estimation of parameters or to parameterize the model and analysis. The positivity and boundedness of the model solution are derived. For the dynamics of the tuberculosis model, we find the equilibrium points and the basic reproduction number. The proposed model is locally and globally stable at disease-free equilibrium, if the reproduction number ℛ0<1. Furthermore, to examine the behavior of the various parameters and different values of fractional-order derivative graphically, the most common iterative scheme based on fundamental theorem and Lagrange interpolation polynomial is implemented. From the numerical result, it is observed that the contact rate and treatment rate have a great impact on curtailing the tuberculosis disease. Furthermore, proper treatment is a key factor in reducing the TB transmission and prevalence. Also, the results are more precise for lower fractional order. The results from various numerical plots show that the fractional model gives more insights into the disease dynamics and on how to curtail the disease spread.

Evaluation of Post-Vaccination Symptoms of Two Common COVID-19 Vaccines Used in Abha, Aseer Region, Kingdom of Saudi Arabia.

BACKGROUND: The Pfizer-BioNTech (BNT162b2) and the Oxford-AstraZeneca (ChAdOx1 nCoV-19) COVID-19 vaccines have shown promising safety and acceptability. However, COVID-19 vaccine side effects play an essential role in public vaccine confidence. We aimed to study the side effects of these COVID-19 vaccines. METHODS: A randomized, cross-sectional descriptive study was conducted between March and May of 2021. In total, 330 participants among the King Khalid University community in the Aseer region of the Kingdom of Saudi Arabia reported their side effects following the COVID-19 vaccine. A questionnaire was designed and validated to collect the participants' demographic data and COVID-19-related symptoms after COVID-19 vaccine injection. RESULTS: Symptoms associated with COVID-19 were reported by 226 participants (68.5%). The most common side effects reported by the participants were fever (n = 136, 41.2%), fatigue (n = 119, 36.1%), headache (n = 86, 24.2%), malaise (n = 121, 36.7%), myalgia (n = 121, 36.7%), and muscle and joint pain (n = 76, 23%). Of the participants, 5.1% became infected with COVID-19 after vaccination. Symptoms were significantly more common in males than in females (p = 0.006). CONCLUSION: The incidence of COVID-19 vaccination side effects in the Aseer region, Kingdom of Saudi Arabia, was consistent with the manufacturers' data. The most common post-vaccination symptoms reported by the participants were fever, myalgia, malaise, fatigue, muscle and joint pain, and headache. The results of this study showed significant variation in adverse events between Pfizer-BioNTech and Oxford-AstraZeneca COVID-19 vaccines. Healthcare providers and recipients of vaccines can be more confident about the safety of Pfizer-BioNTech and Oxford-AstraZeneca COVID-19 vaccines.

A dynamical study of SARS-COV-2: A study of third wave.

The coronavirus still an epidemic in most countries of the world and put the people in danger with so many infected cases and death. Considering the third wave of corona virus infection and to determine the peak of the infection curve, we suggest a new mathematical model with reported cases from March 06, 2021, till April 30, 2021. The model provides an accurate fitting to the suggested data, and the basic reproduction number calculated to be R0=1.2044 . We study the stability of the model and show that the model is locally as well as globally asymptotically stable when R0<1 , for the disease free case. The parameters that are sensitive to the basic reproduction number, their effect on the model variables are shown graphically. We can observe that the suggested parameters can decrease efficiently the infection cases of the third wave in Pakistan. Further, our model suggests that the infection peak is to be May 06, 2021. The present results determine that the model can be useful in order to predict other countries data.