34,354,966 active cases and 460,787 deaths because of COVID-19 pandemic were recorded on November 06, 2021, in India. To end this ongoing global COVID-19 pandemic, there is an urgent need to implement multiple population-wide policies like social distancing, testing more people and contact tracing. To predict the course of the pandemic and come up with a strategy to control it effectively, a compartmental model has been established. The following six stages of infection are taken into consideration: susceptible (S), asymptomatic infected (A), clinically ill or symptomatic infected (I), quarantine (Q), isolation (J) and recovered (R), collectively termed as SAIQJR. The qualitative behavior of the model and the stability of biologically realistic equilibrium points are investigated in terms of the basic reproduction number. We performed sensitivity analysis with respect to the basic reproduction number and obtained that the disease transmission rate has an impact in mitigating the spread of diseases. Moreover, considering the non-pharmaceutical and pharmaceutical intervention strategies as control functions, an optimal control problem is implemented to mitigate the disease fatality. To reduce the infected individuals and to minimize the cost of the controls, an objective functional has been constructed and solved with the aid of Pontryagin’s maximum principle. The implementation of optimal control strategy at the start of a pandemic tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended time period. Extensive numerical simulations show that the implementation of intervention strategy has an impact in controlling the transmission dynamics of COVID-19 epidemic. Further, our numerical solutions exhibit that the combination of three controls are more influential when compared with the combination of two controls as well as single control. Therefore, the implementation of all the three control strategies may help to mitigate novel coronavirus disease transmission at this present epidemic scenario.
Our object in this article is to describe some numerical schemes for the approximation of a Channel Flow problem, and to study the stability of the schemes. Discretization is performed by the combination of spectral methods and finite difference methods. 相似文献
Two new polydentate ligands, 1‐(6‐hydroxymethyl‐2‐pyridyl)‐3‐(2‐pyridyl)‐propane‐1,3‐dione (6) and 1,3‐bis(6‐hydroxymethyl‐2‐pyridyl)‐propane‐1, 3‐dione (8), have been synthesized starting from 2,6‐pyridinedicarboxylic acid by conventional esterification, reduction, and condensation reactions. They were further converted to two new polydentate ligands, 3‐(6‐hydroxymethyl‐2‐pyridyl)‐5‐(2‐pyridyl)‐1H‐pyrazole (7) and 3,5‐bis(6‐hydroxymethyl‐2‐pyridyl)‐1H‐pyrazole (9), by reaction with hydrazine hydrate. These four compounds were characterized by proton nuclear magnetic resonance (1H NMR), electrospray ionization‐mass spectrum (ESI‐MS), infrared spectrum (IR), and elemental analyses. The structure of 9 was also determined by X‐ray diffraction. 相似文献
Control of chemoselectivity is one of the most challenging problems facing chemists and is particularly important in the synthesis of bioactive compounds and medications. Herein, the first highly chemoselective tandem C(sp3)–H arylation/[1,2]-Wittig rearrangement of pyridylmethyl ethers is presented. The efficient and operationally simple protocols enable generation of either arylation products or tandem arylation/[1,2]-Wittig rearrangement products with remarkable selectivity and good to excellent yields (60–99%). Choice of base, solvent, and reaction temperature play a pivotal role in tuning the reactivity of intermediates and controlling the relative rates of competing processes. The novel arylation step is catalyzed by a Pd(OAc)2/NIXANTPHOS-based system via a deprotonative cross-coupling process. The method provides rapid access to skeletally diverse aryl(pyridyl)methanol core structures, which are central components of several medications. 相似文献