Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
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- All Subjects: Actuarial Science
- Creators: School of Mathematical and Statistical Sciences
Actuaries can analyze healthcare trends to determine if rates are reasonable and if reserves are adequate. In this talk, we will provide a framework of methods to analyze the healthcare trend during the pandemic. COVID-19 may influence future healthcare cost trends in many ways. First, direct COVID-19 costs may increase the amount of total experienced healthcare costs. However, with the implementation of social distancing, the amount of regularly scheduled care may be deferred to a future date. There are also many unknown factors regarding the transmission of the virus. Implementing epidemiology models allows us to predict infections by studying the dynamics of the disease. The correlation between infection amounts and hospitalization occupancies provide a methodology to estimate the amount of deferred and recouped amounts of regularly scheduled healthcare costs. Thus, the combination of the models allows to model the healthcare cost trend impact due to COVID-19.
Now I wasn’t the first one to discover the canyon, but I remember a time when I was in the fourth grade. When I stepped out of a bus that I had been in for close to four hours and took forty footsteps to end up at a small brick wall that came close to calf-height which was meant to keep me safe. I don’t know why it didn’t hit me until this point, because I had seen pictures of its grandeur and “experienced” the so called “majesty” of the Grand Canyon through the medium of the National Geographic and tasted of the beauty of one of the natural wonders of the world through the photographs of others before, but standing face to face with a five-thousand-foot cliff humbled me and brought a fear in to me that I can’t describe. Especially when a friend of mine had violently jerked me while I was close to the edge. I remember hearing fear in my father’s voice as I got a little too close to the edge for his comfort. He wanted me to be safe, but I wanted to look this canyon in the eye.
I find it really interesting though, that both my father and I feared ME getting close to the edge. I guess it’s because we both didn’t fully trust my young and feeble knees to keep me stable while I was that close to a fall that would’ve meant sure death for me. Or maybe it was because a couple of months before this, he had seen on the news that some kid was playing too close to the edge and had fallen to his death. Or maybe, it was because, for the first time, death was actually close enough to grasp something he profoundly loved. Either way, I won’t ever forget the loving strain in his voice as he sternly said “Grant! Step a little bit further back from the edge Son.”
It’s really a shame that no one knew. Or at least that no one said anything if they did know. Especially because this New canyon I stood looking face to face with was thousands of feet deeper than the one I had been close to the edge of ten years before, and had the authority to not just kill me once, but twice, if I fell.
The Mack model and the Bootstrap Over-Dispersed Poisson model have long been the primary modeling tools used by actuaries and insurers to forecast losses. With the emergence of faster computational technology, new and novel methods to calculate and simulate data are more applicable than ever before. This paper explores the use of various Bayesian Monte Carlo Markov Chain models recommended by Glenn Meyers and compares the results to the simulated data from the Mack model and the Bootstrap Over-Dispersed Poisson model. Although the Mack model and the Bootstrap Over-Dispersed Poisson model are accurate to a certain degree, newer models could be developed that may yield better results. However, a general concern is that no singular model is able to reflect underlying information that only an individual who has intimate knowledge of the data would know. Thus, the purpose of this paper is not to distinguish one model that works for all applicable data, but to propose various models that have pros and cons and suggest ways that they can be improved upon.