Typically a third year student at our school will work on a small project. This will typically be on research topic that the student is interested in. As while as writing a report for their grades, there are opportunities to present the research at a conference.
This year there are three students in Mathematics at Plymouth, who are making presentations at the British Conference of Undergraduate Research, 12 – 13 April 2021. This year the conference will be virtual. The conference is organized the University of Leeds.
The details about the three talks are below:
Title: What effect does government policy have on the reproductive number for COVID-19?
Student Name: Emily Prestige
The aim of this investigation is to determine the effect of government policies on the transmission of COVID-19 during the period 30/01/2020 – 03/01/2021. This investigation uses both qualitative data in the form of policy legislation and journals, and quantitative data in the form of official government pandemic figures. Key factors of successful government approaches are used to assess the effect of the UK government strategy. The measure used to monitor transmission rates is the instantaneous reproduction number Rt. I used summary statistics, data visualisation, and time series representations, to conduct exploratory data analyses. I also investigated the change in testing capacity and positivity rate over time, to account for factors impacting the number of individuals testing positive for COVID-19. To estimate Rt I used a deterministic Susceptible-Infected-Removed model and a stochastic epidemic model, I then used time series models to predict future behaviour of the positivity rate, transmission rate, and number of new cases. From estimating Rt we see that during lockdowns the rate of transmission is reduced. Predictions show that the positivity rate, transmission rate, and number of new cases, will increase if conditions remain the same, i.e., if further mitigation strategies are not implemented. This investigation finds that when policies were communicated clearly, they had a more significant effect on the reduction of Rt. Furthermore, it also finds several areas which can be improved to enhance the effects of policies on reducing transmission rates.
Title: Analysis on Influencing Factors of Men’s Singles Scores during Winter Olympic Games Cycle
Student Name: Qing Zhang
With the success of Beijing to host the 2022 winter Olympic Games, the main items of the winter Olympics figure skating – also gradually entered people’s field of vision. Figure skating is a highly technical and artistic integration of the ice sports. In combination with the musical accompaniment, the athletes express the musical mood and their own emotions through various ice dance movements, so the sport requires not only the technical ability of the athletes, but also the artistic performance ability of the athletes. This article selects from the International Skating Union (ISU) winter Olympic Games cycle (2014 and 2018) when the man’s world-class competition achievement data statistical analysis, with a number of factors that affect performance results in figure skating men’s as a research perspective, explores how to improve the comprehensive competitiveness of figure skating men’s player of the main ways. The conclusion is that the top factor scores are all the details of the program content, among which the highest score is the performance score; The lower scores were all the details of the technical elements, with the highest score being the jump score. This indicates that the main factors affecting men’s single skating performance in figure skating are program content, including sliding technique, cohesion, performance, arrangement and musical expression. The number and quality of jumps and spins do not significantly affect a competitor’s final result.
Key words: The Winter Olympics; Influencing factors; Regression Analysis; Analysis of Variance; Factor Analysis
Title: Trending Topics and Epidemic Development of the COVID-19 in China: Sentimental Analysis and Visualization
Student name: Yutong Qin
In January 2020, COVID-19 broke out in China. During the Spring Festival, because of the high turnover of people and the infectivity of the virus, the number of COVID-19 confirmed cases is rising rapidly. Led by Weibo, Baidu and Toutiao, with the continuous development of the epidemic, the online public opinion is also changing. To a certain extent, the trending topics represent people’s attitudes and views on the development of epidemic situation. The sentimental analysis of trending topics can comprehend people’s reactions and psychology to the changes of national policies and emergencies, so as to play a guiding role in the countermeasures of similar situations in the future. This paper selects the epidemic data of the NHC of the PRC from January 20 to April 21 in 2020, and the trending topics data of Weibo, Baidu and Toutiao from December 30 of 2019 to April 21 of 2020, to analyze the trending topics’ emotion with the epidemic change. This paper first groups the epidemic development, then turns the word vector of topics into sentence vector. And then uses TF-IDF algorithm to calculate the weight, artificially labels the results with emotion. Sentimental analysis is realized by using CNN, BosonNLP emotion dictionary, and auxiliary dictionary. Finally uses Python and Echarts to make visualization. According to the results of analysis and epidemic data, the paper can get the relationship between public opinions and the development of the epidemic, so as to better comprehend the psychology of public and find out more appropriate countermeasures.