Introduction
For my final project, I wanted to examine how the COVID-19 pandemic has impacted the daily lives of Connecticut residents. I chose to do this by looking at COVID-19 case numbers, vaccination rates, and how the pandemic has affected daily mobility across the state.
Hypothesis
Throughout this exploration, I hope to better understand how the COVID-19 pandemic has shifted the lives of Americans. One of my predictions is that as cases numbers decrease and vaccinations increase, daily mobility will return to its pre-pandemic levels. Inversely, I would expect that as cases increase and the lower vaccine rates are, mobility will drop across the state of Connecticut.
Methodology
It is important to note that all the data being examined in this report are from 2021. While COVID case data is available prior to this, the Google Mobility and vaccine data begin in January 2021. Because of this, I believe it makes the most sense to begin examining all of the data reported from January until November. CT COVID-19 Case Data: https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Hospitalizations-and-Deaths-By-Coun/bfnu-rgqt CT COVID-19 Vaccination Data: https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-County/5g42-tpzq Google Mobility Data: https://www.google.com/covid19/mobility/
Map of Connecticut Counties
First, I want to show a map of the various eight counties in the state of Connecticut that I will focus on.
COVID-19 Cases By County
From this map, we can see that COVID-19 cases appear to be generally at their highest at the beginning of the year until June. The month of June saw the least cases by far across the entire state. From then, cases began to slightly increase.
Statewide COVID-19 Cases
This map shows the overall trend of new found coronavirus cases throughout the entire state. It is clear that cases were at their highest from January until their lowest in June. The warmer months of May, June and July had the state’s lowest number of cases. From June on, they again began to increase. This confirms what was last seen in the map visualization.
COVID-19 Vaccination Percentages By County
In this map, it is evident that as the COVID-19 vaccines became widely available for all to receive, in March and April, Connecticut residents quickly went to receive them. By the end of November, most counties had vaccination rates in the mid-60s. Middlesex County has the highest rate of 72.15% of residents fully vaccinated, and Windham has the lowest with 55.39% of residents fully vaccinated at this time.
Statewide COVID-19 Vaccination Rate
The months February through May saw the highest vaccination rate increase throughout the entire state. This map also shows that by the end of November, 72.15% of the state was fully vaccinated against COVID-19.
Google Mobility Data
After looking at the overall trends of cases and vaccinations against COVID-19, I wanted to further explore the impact of the pandemic on a daily level. In order to do this, I wanted to analyze COVID-19 Google Mobility Data for the state of Connecticut. The COVID-19 pandemic has caused major changes in the way we live our lives and has changed what people do on a daily basis. This dataset analyzes percent changes in how often people go to grocery and pharmacy stores, parks, retail and recreation, transit stations and their workplace from the statewide baseline prior to the pandemic. In this section, I set to understand changes in patterns of various daily activities across the state.
Grocery and Pharmacy Mobility
From these maps, we can see that overall, people went to grocery stores and pharmacies at lower rates than they did before the pandemic began. The months June and September were the most positive with Windham, New London and Middlesex County having averages above the baseline. It is also evident that for some reason, February had the lowest dip in mobility across all counties. From these, we also notice that Fairfield, New Haven, Litchfield, Hartford and Tolland County all consistently witnessed a decrease in mobility, and never once reached their baseline or went above it.
Parks Mobility
In terms of park mobility, overall, there seems to be a general increase in how often people utilized these spaces. My prediction for this is that people felt more comfortable meeting with others in an outdoor setting. Also, after being locked up during quarantine, many learned to value nature and all of its benefits. Also, due to the weather it makes sense why months like January and February have more negative mobility scores attached to them.
Retail & Recreation Mobility
In terms of retail & recreation, the months January, February, August and November were all incredibly negative in terms of mobility across all counties. June was by far the most positive and had four counties exceed their baseline averages. I would not be surprised if this was tied to the vaccine becoming widely available at this point, and having the statewide mask mandate lifted.
Transit Station Mobility
I would attribute the decreases in mobility for transit stations to many workplaces switching to a remote work environment. For example, before the pandemic, many people who live in Fairfield County would often commute to New York City for their jobs. However, many large companies went virtual as a way to keep their employees safe from infection, and this county consistently remained negative in terms of mobility. However, it is fascinating to note that this was not universally true as Tolland County had increases in the amount of time visiting transit stations throughout the year.
Workplace Mobility
As expected, I am not surprised to see that there were only negative changes in the amount that people went to work. Due to new, online work structures, many were able to work from home and did not need to commute to a physical office building often. I would be incredibly interested to see how this changes in the coming year(s) as I believe many jobs will continue offering a remote option in the future to decrease money spent on rent.
Overall Trends
I also wanted to create a visualization that shows all of the statewide trends and averages in an easy way to compare how all of these factors have impacted one another. In terms of mobility, it is evident that parks by far had the most increase in mobility, and that workplaces were by far the least traveled to over the past year. In terms of looking for similarities, the mobility trends looked most similar to the vaccination line chart. This tells me that as more people became vaccinated, they felt more comfortable engaging in more normal daily activities. It is also interesting to note that most activities (other than going to work) had their most mobility during the month of June. This lines up with the idea that people felt more comfortable going out when COVID cases were low. In short, when vaccinations rise and cases fall, people go out more.
NOTE: It is important to take note of the various scales in each section as they can appear deceivingly positive and normal.
Conclusion
Overall, I think that my hypothesis was proven to be mostly accurate. This is seen by the fact that June had the least amount of COVID cases across the state, and the state had reached 64.17% of its residents being fully vaccinated and saw the largest increase in mobility at this time. However, despite the fact that vaccination rates continued increasing over the remainder of the year and cases remained far lower than when the year began, people did not return to their mobility levels seen prior to the pandemic. This tells me that people are more concerned with case numbers in their area rather than the number of people vaccinated around them. These changes could of course be because of fear of the lingering pandemic and changes in daily activities, like not going into a workplace. However, I also think that many have become comfortable with their habits and lifestyle they created with during the pandemic. I would be extremely interested to see how mobility will change in the coming year(s), as I now predict they will continue to rarely hit or exceed pre-pandemic levels of mobility.