Aarupmullins2082

We compute the sensitivity indices of the reproduction number R0 (which quantifies initial disease transmission) to the estimated parameter values. For the estimated model parameters, we obtained R 0 = 1.6632 , which shows the substantial outbreak of COVID-19 in India. Our model simulation demonstrates that the disease transmission rate βs is more effective to mitigate the basic reproduction number R0. Based on estimated data, our model predict that about 60 days the peak will be higher for COVID-19 in India and after that the curve will plateau but the coronavirus diseases will persist for a long time.In this paper, we present a novel fractional order COVID-19 mathematical model by involving fractional order with specific parameters. Selleckchem Yoda1 The new fractional model is based on the well-known Atangana-Baleanu fractional derivative with non-singular kernel. The proposed system is developed using eight fractional-order nonlinear differential equations. The Daubechies framelet system of the model is used to simulate the nonlinear differential equations presented in this paper. The framelet system is generated based on the quasi-affine setting. In order to validate the numerical scheme, we provide numerical simulations of all variables given in the model.COVID-19 pandemic has challenged the world science. The international community tries to find, apply, or design novel methods for diagnosis and treatment of COVID-19 patients as soon as possible. Currently, a reliable method for the diagnosis of infected patients is a reverse transcription-polymerase chain reaction. The method is expensive and time-consuming. Therefore, designing novel methods is important. In this paper, we used three deep learning-based methods for the detection and diagnosis of COVID-19 patients with the use of X-Ray images of lungs. For the diagnosis of the disease, we presented two algorithms include deep neural network (DNN) on the fractal feature of images and convolutional neural network (CNN) methods with the use of the lung images, directly. Results classification shows that the presented CNN architecture with higher accuracy (93.2%) and sensitivity (96.1%) is outperforming than the DNN method with an accuracy of 83.4% and sensitivity of 86%. In the segmentation process, we presented a CNN architecture to find infected tissue in lung images. Results show that the presented method can almost detect infected regions with high accuracy of 83.84%. This finding also can be used to monitor and control patients from infected region growth.The goal of this work is to study the optimal controls for the COVID-19 epidemic in Brazil. We consider an age-structured SEIRQ model with quarantine compartment, where the controls are the quarantine entrance parameters. We then compare the optimal controls for different quarantine lengths and distributions of the total control cost by assessing their respective reductions in deaths in comparison to the same period without quarantine. The best strategy provides a calendar of when to relax the isolation measures for each age group. Finally, we analyse how a delay in the beginning of the quarantine affects this calendar by changing the initial conditions.We propose an SEIARD mathematical model to investigate the current outbreak of coronavirus disease (COVID-19) in Mexico. Our model incorporates the asymptomatic infected individuals, who represent the majority of the infected population (with symptoms or not) and could play an important role in spreading the virus without any knowledge. We calculate the basic reproduction number (R0) via the next-generation matrix method and estimate the per day infection, death and recovery rates. The local stability of the disease-free equilibrium is established in terms of R0. A sensibility analysis is performed to determine the relative importance of the model parameters to the disease transmission. We calibrate the parameters of the SEIARD model to the reported number of infected cases, fatalities and recovered cases for several states in Mexico by minimizing the sum of squared errors and attempt to forecast the evolution of the outbreak until November 2020.The cumulative number of confirmed infected individuals by the new coronavirus outbreak until April 30th, 2020, is presented for the countries Belgium, Brazil, United Kingdom (UK), and the United States of America (USA). After an initial period with a low incidence of newly infected people, a power-law growth of the number of confirmed cases is observed. For each country, a distinct growth exponent is obtained. For Belgium, UK, and USA, countries with a large number of infected people, after the power-law growth, a distinct behavior is obtained when approaching saturation. Brazil is still in the power-law regime. Such updates of the data and projections corroborate recent results regarding the power-law growth of the virus and their strong Distance Correlation between some countries around the world. Furthermore, we show that act in time is one of the most relevant non-pharmacological weapons that the health organizations have in the battle against the COVID-19, infectious disease caused by the most recently discovered coronavirus. We study how changing the social distance and the number of daily tests to identify infected asymptomatic individuals can interfere in the number of confirmed cases of COVID-19 when applied in three distinct days, namely April 16th (early), April 30th (current), and May 14th (late). Results show that containment actions are necessary to flatten the curves and should be applied as soon as possible.The outbreak of COVID-19 caused by SARS-CoV-2 is spreading rapidly around the world, which is causing a major public health concerns. The outbreaks started in India on March 2, 2020. As of April 30, 2020, 34,864 confirmed cases and 1154 deaths are reported in India and more than 30,90,445 confirmed cases and 2,17,769 deaths are reported worldwide. Mathematical models may help to explore the transmission dynamics, prediction and control of COVID-19 in the absence of an appropriate medication or vaccine. In this study, we consider a mathematical model on COVID-19 transmission with the imperfect lockdown effect. The basic reproduction number, R0, is calculated using the next generation matrix method. The system has a disease-free equilibrium (DFE) which is locally asymptotically stable whenever R0  less then  1. Moreover, the model exhibits the backward bifurcation phenomenon, where the stable DFE coexists with a stable endemic equilibrium when R0  less then  1. The epidemiological implications of this phenomenon is that the classical epidemiological requirement of making R0 less than unity is only a necessary, but not sufficient for effectively controlling the spread of COVID-19 outbreak.