The article deals with the analysis of the fractional COVID-19 epidemic model (FCEM) with a convex incidence rate. Keeping in view the fading memory and crossover behavior found in many biological phenomena, we study the coronavirus disease by using the noninteger Caputo derivative (CD). Under the Caputo operator (CO), existence and uniqueness for the solutions of the FCEM have been analyzed using fixed point theorems. We study all the basic properties and results including local and global stability. We show the global stability of disease-free equilibrium using the method of Castillo-Chavez, while for disease endemic, we use the method of geometrical approach. Sensitivity analysis is carried out to highlight the most sensitive parameters corresponding to basic reproduction number. Simulations are performed via first-order convergent numerical technique to determine how changes in parameters affect the dynamical behavior of the system.This paper presents a novel model to detect the COVID-19 infected person from a Markovian feedback persons in a limited department capacity. The persons arrive one by one to the department and the balking and the retention of reneged person approaches are considered. There exists one server presents the service to these persons according to first-come, first-served (FCFS) discipline. An efficient and novel algorithm is presented to get the exact value of the probability of n persons in the department at any time interval. This algorithm depends on the Laplace transform to solve a probabilistic dynamical system of differential equations. By considering the exponential detection function and if the probability of the infected person in the department is equal to the probability of each one, then this algorithm is useful to obtain the detection probability of the infected one. Under steady state, the detection probability of the infected person is described. The usefulness of this model is illustrated for different capacities by using a numerical example to describe the behavior of probabilities of the persons in the department, the detection probabilities of the infected person as functions in time, and the mean time to detection.Coronavirus disease 2019 and related lockdown policies in 2020 shocked food industry firms' supply chains in developing regions. Firms "pivoted" to e-commerce to reach consumers and e-procurement to reach processors and farmers. "Delivery intermediaries" copivoted with food firms to help them deliver and procure. This was crucial to the ability of the food firms to pivot. The pandemic was a "crucible" that induced this set of fast-tracking innovations, accelerating the diffusion of e-commerce and delivery intermediaries, and enabling food industry firms to redesign, at least temporarily, and perhaps for the long term, their supply chains to be more resilient, and to weather the pandemic, supply consumers, and contribute to food security. We present a theoretical model to explain these firm strategies, and then apply the framework to classify firms' practical strategies. We focus on cases in Asia and Latin America. Enabling policy and infrastructural conditions allowed firms to pivot and copivot fluidly.In this paper we descriptively investigate the Covid-19 pandemic's early impact on the fruit and vegetable supply chain in Senegal, using trade statistics and survey data collected through online questionnaires and telephone interviews with smallholder farmers, agro-industrial companies, agricultural workers, traders, importers, and consumers. Our results point to major differences in how Covid-19 and containment measures disrupt supply chains between the modern export-oriented supply chain that is centered around a few large vertically integrated agro-industrial companies, and the more traditional domestic-oriented supply chain with a large number of smallholder farmers and informal traders-with the former being more resilient to the Covid-19 shock. We show that both the modern and the traditional supply chain innovate to cope with the Covid-19 containment measures. While our study is subject to some limitations, our findings bring nuance in the debate on the resilience of the food system to the pandemic, and have important policy and research implications toward international trade, social safety measures, and food and nutrition security.This study assesses the impact of coronavirus disease 2019 (COVID-19) on poverty, food insecurity, and diets, accounting for the complex links between the crisis and the incomes and living costs of vulnerable households. Key elements are impacts on labor supply, effects of social distancing, shifts in demand from services involving close contact, increases in the cost of logistics in food and other supply chains, and reductions in savings and investment. These are examined using IFPRI's global general equilibrium model linked to epidemiological and household models. The simulations suggest that the global recession caused by COVID-19 will be much deeper than that of the 2008-2009 financial crisis. The increases in poverty are concentrated in South Asia and sub-Saharan Africa with impacts harder in urban areas than in rural. The COVID-19-related lockdown measures explain most of the fall in output, whereas declines in savings soften the adverse impacts on food consumption. Almost 150 million people are projected to fall into extreme poverty and food insecurity. Decomposition of the results shows that approaches assuming uniform income shocks would underestimate the impact by as much as one-third, emphasizing the need for the more refined approach of this study.We study how individual decisions are affected by those of other members of the society.  https://www.selleckchem.com/products/pexidartinib-plx3397.html  We use the vaccine against COVID-19 as a case study and empirically estimate the magnitude of three key forces Herding, Social Norms, and Free-riding. We find that Free-riding is dominated by the other two forces, and that Social Norms are a key driver of behavior. There is, however, substantial heterogeneity and systematic differences between people by demographics and their political preferences.The objective prevalence of and subjective vulnerability to infectious diseases are associated with greater ingroup preference, conformity, and traditionalism. However, evidence directly testing the link between infectious diseases and political ideology and partisanship is lacking. Across four studies, including a large sample representative of the U.S. population (N > 12,000), we demonstrate that higher environmental levels of human transmissible diseases and avoidance of germs from human carriers predict conservative ideological and partisan preferences. During the COVID-19 pandemic (N = 848), we replicated this germ aversion finding and determined that these conservative preferences were primarily driven by avoidance of germs from outgroups (foreigners) rather than ingroups (locals). Moreover, socially conservative individuals expressed lower concerns of being susceptible to contracting infectious diseases during the pandemic and worried less about COVID-19. These effects were robust to individual-level and state-level controls.