The Effects of Outdoor Air Pollutants on the Costs of Stroke Hospitalizations in China

Stroke, the most frequent cause of severe disability and the second cause of death among adults in the world, brings tremendous mental and economic burden to patients and their families. Emerging evidence indicates that the air pollution mixture contributes to strokes. Knowing the relationship between the air pollution and the hospital costs of stroke can help us predict the costs due to air pollution, provide grounds for the allocation of medical insurance funds, and provide better working arrangements for CDC. However, few studies have examined this connection. We used time series analysis with a generalized additive model to estimate the associations between ambient air pollutions and hospital costs between the period of 2015–2017. We were surprised to find that although same-day air pollutions were positively associated with stroke mortality hospital costs were found to have a negatively association. Suggestive evidence of an association between fine particles and the costs of stroke were found: more serious air pollution increases the risk of stroke, but has a dampening effect on hospital costs. This study is the first step in optimizing medical resources, which is essential for policy making, service planning, and cost-effectiveness analysis of new therapeutic strategies

Key Elements For A Successful Employee Onboarding Program

This capstone focuses on examining key elements of employee-onboarding success in organizations. From the literature, the author extracted three crucial factors contributing to effective onboarding models, Relationship-Building, Organizational Support, and Communication, and proposed a hypothesis: These three key elements are contributing to onboarding success. Then, the author identified four outstanding employee onboarding models from Netflix, Twitter, LinkedIn, and Google, by using the criteria from several human resource specialized websites. In the analysis, the author examined the three elements in the four onboarding models and found out that all the factors contributed to employee onboarding success individually and collectively. Furthermore, the author proposed that “People” is a core value of a successful employee onboarding process for organizations. This paper strongly suggests that it would be worth pursuing with a larger study to examine the three elements and extract more factors

Abstractive Text Summarization for Tweets

In the high-tech age, we can access a vast number of articles, information, news, and opinion online. The wealth of information allows us to learn about the topics we are interested in more easily and cheaply, but it also requires us to spend an enormous amount of time reading online. Text summarization can help us save a lot of reading time so that we can know more information in a shorter period. The primary goal of text summarization is to shorten the text while including as much vital information as possible in the original text so fewer people use this strategy on tweets since tweets are commonly shorter than articles or news. However, as social networking software becomes more widespread, Text summarization can assist us in swiftly reviewing a large number of comments and discussions. In this project, we applied fuzzy logic and a neural network to extract essential sentences, followed by an abstraction model to provide a summary. Summaries generated by our model contain more vital content and obtain a better ROUGE score than classic abstraction models since we extract the crucial information first; summaries generated by our model are more similar to human-written summaries than traditional extraction models because we are using an abstract model. In the end, we provided a web-based application to display our model more interactively

Data integration and simulation of population immunity at the beginning of a pandemic

Accurate knowledge of population exposure at the outset of a pandemic has critical ramifications for preparedness plans for future epidemic waves. In this thesis, I developed a mechanistically informed statistical model to integrate multiple epidemiological datasets in different settings and in different population and to estimate key epidemiological parameters as well as population exposure using Bayesian inference. First, I present a dynamic model to link together three key metrics for evaluating the progress of COVID-19 epidemic in England: seroprevalence, PR-PCR test positivity and death. While estimating the IgG antibody seroreversion rate and region-specific infection fatality ratios, I find that epidemic progression resulted in an increasing gap between measured serology prevalence levels and cumulative population exposure to the virus. Ultimately, this may mean that twice as many, or more, people have been exposed to the virus relative to the number of people who are seropositive by the end of 2020. Moreover, I demonstrate that the model could reconstruct the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 as long as two or three serological measurements are given as inputs, with the second wave during the winter of 2020 validated by the estimates from the ONS Coronavirus Infection Survey. Comparing with the inferred exposure, I find that the UK official COVID-9 online dashboard reported COVID-19 cases only accounted for less than ten percent by the end of October 2020. I then generalise the model to account for the undocumented COVID-19-related mortality and sparse measurements of seroprevalence. I apply this in the context of Afghanistan COVID- 19 epidemic and find the population exposure in nine regions of Afghanistan were all higher than the seroprevalence survey suggested by July 2020. Finally, I assess the impact of shielding among pregnant patients by comparing their exposure with the estimated exposure of the general population. To approach this, I develop a dynamic model to link RT-PCR and antibody testing results from patients who gave birth and then apply Bayesian inference to estimate transmission parameters and exposure among pregnant patients. I find that after considering the duration of each pregnancy pre-COVID onset and after, the impact of shielding on reducing the level of exposure among pregnant patients during early 2020 who gave birth in this New York City hospital were approximately 50%