Introduction

Returning to college during a pandemic holds many challenges for the university and the students. With being a student at Elon University, I wanted to examine the effectiveness of Elon’s Ready and Resilient Plan that has been put in place to guide on-campus classes and operations of the upcoming year [2]. This plan was announced in June of 2020 by our President Connie Ledoux Book and was curated based on recommendation of the Task-Force that was put in place in April of 2020 to examine the safe return to campus. Elon’s Ready & Resilient plan implemented many changes due to the severity of the pandemic. Key takeaways from the Ready & Resilient plan included a return to in-person instruction, a modified university calendar for Fall 2020, a public health plan and adjusted policies, reimagined classes and teaching spaces, as well as an adjustment to most campus activities and operations [2].

In order to examine the effectiveness of Elon’s Ready & Resilient plan, we can examine the number of covid-19 cases for North Carolina as a state and even more specifically Alamance County, where Elon University is located. Once we look at these two factors, we then can compare Elon University covid data to Elon’s Peer Institutions to see if other schools with the same size and scope are handling this pandemic better or worse. Comparing the number of covid-19 cases with Elon’s peer institutions will allow us to examine the effectiveness of our Ready & Resilient plan since these peer institutions are similar to Elon on a multitude of pre-determined factors.

Hypothesis

When compared to similar institutions, I believe Elon University’s Ready & Resilient plan was ineffective in handling the return of students on campus.

COVID19 By States

Throughout the pandemic, covid-19 impacted states in a variety of multitudes. For example, New York City was known to be a hot spot for the pandemic during March [3]. During the pandemic, each state had their own rules and guidelines regarding quarantine and the reopening of businesses. Along with rules regarding covid, each state also records and collects the covid cases data differently. All of this information is important to note when examining the number of covid-19 cases per state.

When examining the effectiveness of the Ready & Resilient plan, it is first important to examine how the pandemic effected North Carolina, where Elon University is located, in order to assess how the surrounding cities and areas were effected. Using the state covid data from the New York Times, we can use Tableau to create a visualization that shows the number of cases in each state over a certain time period.

For this visualization, it is important to note that just the continental United States is being examined since overall this report is only focused on Elon University. Only looking at continental US doesn’t impact this report. Also, it is important to note the time frame of this visualization. Since this report was created mid October, it wouldn’t be valid to show half of a months data in the visualization.

When examining North Carolina, the state in which Elon University is located in, we can see that the number of cases per month are similar to North Carolina’s surrounding states. The visualization above shows that other state such as New York, Florida, Texas and California reported a much larger number of covid-19 cases per month when compared to North Carolina. However, when looking at number of cases per state, it is important to factor in population. A more populous state will in theory report more cases due to the increased amount of people in one area. The above visualization also shows the population by state. Showing the population per state supports the idea that more populated states show a higher number of covid cases.

This visualization allows us to determine that North Carolina did not report as many cases when compared to other US states. In terms of the Ready & Resilient plan, this could mean that Elon did not create such a strict return plan when compared to other colleges in states where more cases were reported. Again, this is all something to consider when examining the effectiveness of Elon University’s Ready & Resilient plan.

COVID19 By County

When examining the effectiveness of Elon’s Ready & Resilient plan, along with examining covid by state, it is also important to examine the number of covid cases per county. Using the covid by county data from the New York Times, we again can create a visualization using Tableau to see the number of cases in each county every month, starting in January. Similar to the previous visualization, only the continental United States is being examined as well as not including October, for the same reasons previously stated.

Since Elon University is located in Alamance County, we can use the highlight feature to specifically examine the number of covid-19 cases in Alamance County over time. Even though Alamance County shows an increase in cases over each month, the number of cases is relatively small when compared to other counties in New York, Florida, and California. These results mimic the previous visualization but differ since this visualization is more specific in it’s data.

State and County Recap

Overall, when comparing North Carolina’s number of reported covid-19 cases over the past few months, we can conclude that North Carolina did not appear to be a hot spot for covid-19. It is necessary that we explored how covid-19 affected North Carolina as a state since institutions in each state may have handled reopening differently.To look at the number of covid cases in the surrounding area of Elon more specifically, we examined covid-19 cases per county. These results mimicked the results of the number of covid cases per state. Overall, we are able to determine that Elon Universities surrounding area was not a hot spot for covid-19 during the time frame of January to September of 2020.

Peer Institutions

Since we have determined that Elon University was not located in a hot spot for covid-19, now let us examine how Elon University’s covid-19 cases compare to institutions similar to Elon. Elon’s Peer Institutions were determined by a plethora of individuals who include academic council members, alumni, academic units, senior staff, board of trustees, students, and staff [1]. The 15 listed institutions relate to Elon University since they are comparable in terms of size, scope, and resources. The list of Elon’s peer institutions can be found on Elon’s Website.

Elon’s Universities Peer Institutions:

  1. Bucknell University (PA)
  2. Butler University (IN)
  3. College of Charleston (SC)
  4. College of William & Mary (VA)
  5. Creighton University (NE)
  6. Davidson College (NC)
  7. Furman University (SC)
  8. Ithaca College (NY)
  9. James Madison University (VA)
  10. Lehigh University (PA)
  11. Loyola University (MD)
  12. Rollins College (FL)
  13. Santa Clara University (CA)
  14. University of Richmond (VA)
  15. Villanova University (PA)

Comparing the number of covid-19 cases of these institutions to Elon, will allow us to determine if Elon’s Ready & Resilient plan was effective. To do this, I used the New York Times college covid data. This data set collects the number of covid-19 cases reported by the institution every two weeks and accumulates them into one number. This data set includes every four-year public institution and every private college that competes in N.C.A.A sports and is what I have used to collect the number of covid-19 cases for each of Elon’s peer institutions [4]. This data set began collecting the number of cases reported since late July.

To visualize the number of reported covid cases per institution, I first needed to import the correct packages.

library(tidyverse)
library(tidytext)
library(ggthemes)
library(wordcloud2)
library(textdata)
library(gridExtra)
library(readr)
library(kableExtra)

Then, using the covid college data from the New York Times, I was then able to filter out institutions and examine only Elon’s Peer Institutions. I did this using the filter() function.

  covid_colleges %>% 
  filter(college %in% c("Bucknell University","Butler University",
                      "College of Charleston","William & Mary","Creighton University",
                      "Davidson College","Furman University","Ithaca College",
                      "James Madison University","Lehigh University",
                      "Loyola University Maryland", "Rollins College",
                      "Santa Clara University","University of Richmond",
                      "Villanova University","Elon University")) %>% 
  arrange(desc(cases))-> elon_peerInstitutions
college state cases
James Madison University Virginia 1591
College of Charleston South Carolina 432
Creighton University Nebraska 365
Elon University North Carolina 310
Villanova University Pennsylvania 235
Lehigh University Pennsylvania 212
Butler University Indiana 165
Furman University South Carolina 90
Santa Clara University California 35
Rollins College Florida 27
Ithaca College New York 25
Davidson College North Carolina 25
University of Richmond Virginia 25
Bucknell University Pennsylvania 19
William & Mary Virginia 17
Loyola University Maryland Maryland NA

The table above allows us to see how Elon is compared to it’s 15 other peer institutions. Elon University reports the 4th highest number of covid-19 cases when compared to its 15 other peer institutions. As of October 23rd, 2020, Elon reported 310 cases. Even though this number seems relatively low compared to the millions of cases across the country, let’s see how this number compares to the other institutions. Off this table, it is clear to see James Madison University reports an extreme value of 1,591 cases. It is clear that some institutions reported similar numbers of cases when compared to Elon. However, 8 out of the 15 of theses institutions reported less than 100 cases. To visualize theses cases, we can create a bar chart in order to see the number of cases more clearly. To do this, I used ggplot(). It is important to note that I filtered out Loyola University since they did not report any data since their cases data value is NA or null. This does not mean they reported 0 cases, it means they are not publicly share their covid-19 data.

 elon_peerInstitutions %>% 
  filter(!college %in% "Loyola University Maryland") %>% 
  ggplot(aes(reorder(college,cases),cases)) + geom_col(fill ="red" ) + coord_flip()+
  theme_economist() + ylab("Number of Cases") + xlab("Institution Name") +
  geom_text(aes(label=cases), hjust =0.4,vjust=0, color="black", size=3.5, fontface = "bold") +      labs(title = "Number of Covid-19 Cases per Institution", subtitle = "Elon Universities Peer Institutions")

This graph allows us to visualize the number of covid-19 cases easier for each institution. Here we can see how Elon compares to its peer institutions. This graph shows that 11 out of 15 institutions reported a lower number of covid cases. In order to examine the effectiveness of Elon’s plan, it is important to look at the institutions that reported a smaller number of covid cases. Specifically, I find it interesting that Santa Clara University, Rollins College, and Ithaca College all reported less cases than Elon.

elon_peerInstitutions %>% 
    filter(college %in% c("Santa Clara University", 
                          "Elon University",
                          "Ithaca College", 
                          "Rollins College")) %>% 
  arrange(desc(cases)) -> hot_spot_colleges
state college cases
North Carolina Elon University 310
California Santa Clara University 35
Florida Rollins College 27
New York Ithaca College 25

These schools are important to analyze since these institutions are all located in hot spots of covid-19, which we determined in the previous visualizations. These three institutions are located in California, Florida, and New York respectively. This means that the states these institutions are located in, experienced a significant amount of covid cases over the past few months but, the schools only reported a small amount of cases. One then wonders how these schools were able to keep the covid cases so limited. Did they have strict restrictions when having their students return to campus? Did they even allow students to return to campus? Do they conduct mostly virtual classes? These are all questions to consider when examining this data.

Conclusion

When examining the number of covid-19 cases per institution, we were able to show that Elon ranked high in the number of cases when compared to it’s peer institutions. This raises the question as to why Elon reported a significant amount of cases when other similar colleges located in hot spots reported fewer cases. Since there are many aspects to consider when examining all the colleges, we can not draw a direct comparison between the number of cases and the effectiveness of Elon’s Ready & Resilient plan. However, it is interesting to further explore this topic and look into each institution to see how they are handling covid and their guidelines and restrictions in place in order to get a better comparison between Elon and other universities.