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The worldwide outbreak of the coronavirus was first identified in 2019 in Wuhan, China. Since then, the disease has spread worldwide. As it is currently spreading in the United States, policy makers, public health officials and citizens are racing to understand the impact of this virus on the United States healthcare system. They fear a rapid influx of patients overwhelming the healthcare system and leading to unnecessary fatalities. Most countries and states in America have introduced mitigation strategies, such as using social distancing to decrease the rate of newly infected people. This is what is usually meant by flattening the curve. In this paper, we use queueing theoretic methods to analyze the time evolution of the number of people hospitalized due to the coronavirus. Given that the rate of new infections varies over time as the pandemic evolves, we model the number of coronavirus patients as a dynamical system based on the theory of infinite server queues with time inhomogeneous Poisson arrival rates. With this model we are able to quantify how flattening the curve affects the peak demand for hospital resources. This allows us to characterize how aggressive societal policy needs to be to avoid overwhelming the capacity of healthcare system. We also demonstrate how curve flattening impacts the elapsed lag between the times of the peak rate of hospitalizations and the peak demand for the hospital resources. Finally, we present empirical evidence from Italy and the United States that supports the insights from our model analysis. [ABSTRACT FROM AUTHOR]

A recent paper showed that, whereas we expect pain to rise with age due to accumulated injury, physical wear and tear, and disease, the elderly in America report less pain than those in midlife. Further exploration revealed this pattern was confined to the less educated. The authors called this the 'mystery of American pain' since pain appears to rise with age in other countries irrespective of education. Revisiting this issue with the same cross-sectional data we show that what matters in explaining pain through to age 65 is whether one is working or not. The incidence of pain across the life-course is nearly identical for workers in America and elsewhere, but it is greater for non-working Americans than it is for non-workers elsewhere. As in other countries, pain is hump-shaped in age among those Americans out of work but rises a little over the life-course for those in work. Furthermore, these patterns are apparent within educational groups. We show that, if one ascribes age-specific employment rates from other OECD countries to Americans, the age profile of pain in the United States is more similar to that found elsewhere in the OECD. This is because employment rates are lower in the United States than elsewhere between ages 30 and 60: the simulation reduces the pain contribution of these non-workers to overall pain in America, so it looks somewhat similar to pain elsewhere. We conclude that what matters in explaining pain over the life-course is whether one is working or not and once that is accounted for, the patterns are consistent across the United States and the rest of the OECD. [ABSTRACT FROM AUTHOR]