Course Project: How U.S. COVID-19 Vaccinations Have Varied Over Time in Each Age Bracket
Project Proposal
Our project examines the data collected on COVID-19 vaccinations of U.S. residents, and more specifically by the age brackets obtained by the CDC. Our overarching goal in our analysis is to determine how U.S. vaccination rates in different age brackets have varied over time. The COVID-19 vaccine was rolled out in phases, some of which had age restrictions. A Gannt chart of this timeline is shown in Figure 1. These dates will affect our expectations for vaccination rates by age category. Accordingly, we will factor these skewed numbers into our analysis given the rollout timeline. We are more specifically going to examine the vaccination rates of age groups next to their corresponding eligibility date to see if there was a spike in vaccinations immediately following the eligibility date, or whether there was a delay. In our data there will be varying age groups being vaccinated, even from the very beginning. Although age restrictions were in phases 1b and 1c, other eligible groups such as healthcare workers had the potential to include younger age brackets (Dooling, MD et al.). For this reason, we assume that our data will be spread out among age brackets, and we will carefully consider the vaccine eligibility timeline when drawing conclusions.
“Vaccine hesitancy remains a barrier to full population inoculation against highly
infectious diseases” (Dror et al.). The U.S. is facing this problem right now regarding the COVID- 19 vaccines and may continue to face it about healthcare decisions in the future. Performing descriptive analytics on vaccination rates by age bracket is a first step in how to remedy this
issue. With our project’s summaries, prescriptive analytics can be applied as a next step to target groups with lower vaccination rates. Although many demographics take part in affecting vaccination rates and vaccine hesitancy, age is a broad place to begin that may encompass many different factors contributing to this hesitancy. Understanding this data will play an important role in speeding up the process of herd immunity.
We acquired our raw dataset, “COVID-19 Vaccination Demographics in the United States, National” from the official Centers for Disease Control and Prevention (CDC) website. It is a real-time update dataset which contains records of vaccinations and vaccine ratios in the United States from December 13, 2020 to the present day. Although the dataset is continuously updating, for our project’s purposes we decided to analyze data from 12/13/20 through
11/12/21. There are various types of data such as, “administered_dose1_pct_known” which represent the percent among persons with at least one dose who are Hispanic/Latino. The data found in these columns were incomplete and not useful to our future analysis. Since the only two phenomena we decided to examine are the number of vaccinated people by age group for both part ...
1. Course Project: How U.S. COVID-19 Vaccinations Have Varied
Over Time in Each Age Bracket
Project Proposal
Our project examines the data collected on COVID-19
vaccinations of U.S. residents, and more specifically by the age
brackets obtained by the CDC. Our overarching goal in our
analysis is to determine how U.S. vaccination rates in different
age brackets have varied over time. The COVID-19 vaccine was
rolled out in phases, some of which had age restrictions. A
Gannt chart of this timeline is shown in Figure 1. These dates
will affect our expectations for vaccination rates by age
category. Accordingly, we will factor these skewed numbers
2. into our analysis given the rollout timeline. We are more
specifically going to examine the vaccination rates of age
groups next to their corresponding eligibility date to see if there
was a spike in vaccinations immediately following the
eligibility date, or whether there was a delay. In our data there
will be varying age groups being vaccinated, even from the very
beginning. Although age restrictions were in phases 1b and 1c,
other eligible groups such as healthcare workers had the
potential to include younger age brackets (Dooling, MD et al.).
For this reason, we assume that our data will be spread out
among age brackets, and we will carefully consider the vaccine
eligibility timeline when drawing conclusions.
“Vaccine hesitancy remains a barrier to full population
inoculation against highly
infectious diseases” (Dror et al.). The U.S. is facing this
problem right now regarding the COVID- 19 vaccines and may
continue to face it about healthcare decisions in the future.
Performing descriptive analytics on vaccination rates by age
bracket is a first step in how to remedy this
issue. With our project’s summaries, prescriptive analytics can
be applied as a next step to target groups with lower vaccination
rates. Although many demographics take part in affecting
vaccination rates and vaccine hesitancy, age is a broad place to
begin that may encompass many different factors contributing to
this hesitancy. Understanding this data will play an important
role in speeding up the process of herd immunity.
We acquired our raw dataset, “COVID-19 Vaccination
Demographics in the United States, National” from the official
Centers for Disease Control and Prevention (CDC) website. It is
a real-time update dataset which contains records of
vaccinations and vaccine ratios in the United States from
December 13, 2020 to the present day. Although the dataset is
continuously updating, for our project’s purposes we decided to
analyze data from 12/13/20 through
11/12/21. There are various types of data such as,
“administered_dose1_pct_known” which represent the percent
3. among persons with at least one dose who are Hispanic/Latino.
The data found in these columns were incomplete and not useful
to our future analysis. Since the only two phenomena we
decided to examine are the number of vaccinated people by age
group for both partially and fully vaccinated individuals, we
used Python’s data analysis tools through Jupyter Notebook to
eliminate unneeded and redundant data columns. There are
around twenty different categories under each date, which
include not only age groups but different races and genders as
well. As previously mentioned, our goal is to determine how
U.S. vaccination rates in different age brackets have varied over
time. Thus, we only kept the data representing vaccinations in
different age group categories. Finally, we reorganized the
remaining data into two new CSV data frames containing
information on one administered dose and fully vaccinated
individuals, respectively. Under each data frame, we put dates
and different age-groups under the X and Y axis and filled them
with the number of first and second doses. With the modified
data frames, we will be able to begin our initial analysis which
is discussed below.
Page 2 | Team 1 | How U.S. COVID-19 Vaccinations Have
Varied Over Time in Each Age Bracket
Fig. 1. “Covid Vaccine Eligibility by Date in the United States”
by Olivia Verni
(Dooling, MD et al.), (Office of the Commissioner)
4. Fig. 2. “Number of Fully Vaccinated People in the U.S. as of
November 12, 2021 by Age Bracket”
by Olivia Verni
Page 3 | Team 1 | How U.S. COVID-19 Vaccinations Have
Varied Over Time in Each Age Bracket
To gain an initial understanding of our data, our group decided
to use the descriptive statistics tool in Microsoft Excel to
compute the mean amount of fully vaccinated individuals by
different ranges of age. This tool will be a more efficient way of
calculating the averages
compared to the approach by using the “AVERAGE” function.
The next step is to visualize the means that we have computed.
The bar chart will be the most efficient way to demonstrate our
data clearly.
Our first visualization is shown in Figure 3 represents the mean
vaccinations per age group from our first month, December
2020. U.S. residents were just being notified of eligibility for
vaccinations, leaving a very small population that on our graph
did not exceed 1000 people in any age group. Individuals of age
above 50-64 yrs and 25-39 yrs are the main group to have a full
series completion of vaccination in December 2020. This is
likely due to healthcare and long-term care residents being
eligible. Ages 75+ opened up later in the month of December,
making their vaccination numbers lower than that of other
groups. Oppositely, the teenager’s group was not participating
that much due to their limited eligibility.
We also examined data from June 2021, Figure 4, about half
way through our timeline to show comparison. With the
liberalization of the vaccination policy, the amounts of
5. vaccinations had a phenomenal increase in all categories of ages
as expected. Age group 50-64 yrs still has the most
vaccinations. This could be due to their eligibility at the time,
but also may have to do with their age group being in the “baby
boomer” population, which is overall a larger age group. All
age groups of people, now including teenagers, are actively
starting to get involved in vaccination at this point.
Our last chart in November 2021, Figure 5, is the most present
data we have. The unit for vaccinated individuals is measured in
millions, showing great progress in vaccination. The individuals
of age 50-64 yrs are still the most prominent group. Due to this
analysis, it is clear that we may need to account for the total
population of each age group to truly be able to compare.
Fig. 3. “Number of Fully Vaccinated People in the U.S. as of
December, 2020 by Age Bracket” by
Zilong Wu
Page 3 | Team 1 | How U.S. COVID-19 Vaccinations Have
Varied Over Time in Each Age Bracket
Fig. 4. “Number of Fully Vaccinated People in the U.S. as of
June, 2021 by Age Bracket” by
Zilong Wu
Fig. 5. “Number of Fully Vaccinated People in the U.S. as of
November, 2021 by Age Bracket” by
Zilong Wu
6. Figure 6 below shows that the 50-64 yrs age group has the
largest number of individuals vaccinated by November 2021.
We may be seeing a high number of vaccinations for this age
group as their risk for coronavirus is relatively high. Or as
previously mentioned, it may be due to their large size as a
generation. As time goes by, the number of people over 50 years
of age who are vaccinated has increased significantly, followed
by the 25-39 age group. The 16-17 age group had the fewest
participants, which is expected due to their limited options of
vaccines and the eligibility restrictions that were present for
most of the year. From the perspective of the development of
the US epidemic, the proportion of children's cases is increasing
significantly, from 2% in the early stage of the epidemic to 7%
(CDC Information for Pediatric Healthcare Providers). Although
the incidence rate is still lower than that of adults, or the
Page 3 | Team 1 | How U.S. COVID-19 Vaccinations Have
Varied Over Time in Each Age Bracket
severity of the disease is significantly lower than that of adults,
children may play an important role in spreading the virus. As
the chart shows, children under 12 have very low vaccinati on
rates, but is expected as they only became eligible November 1,
2021.
Fig. 6. “Number of Individuals with a Single Dose in the U.S.
Over Time Age Bracket”
7. Works Cited
Dooling, MD, Kathleen, et al. “The Advisory Committee on
Immunization Practices' Updated Interim Recommendation for
Allocation of COVID-19 Vaccine - United States, December
2020.” Centers for Disease Control and Prevention, Centers for
Disease Control and Prevention, 31 Dec. 2020,
https://www.cdc.gov/mmwr/volumes/69/wr/mm695152e2.htm.
Commissioner, Office of the. “Coronavirus (COVID-19)
Update: FDA Authorizes Pfizer- Biontech COVID-19 Vaccine
for Emergency Use in Adolescents in Another Important Action
in Fight against Pandemic.” U.S. Food and Drug
Administration, FDA, 10 May 2021, https://www.fda.gov/news-
events/press-announcements/coronavirus-covid- 19-update-fda-
authorizes-pfizer-biontech-covid-19-vaccine-emergency-use.
Commissioner, Office of the. “FDA Authorizes Pfizer-Biontech
COVID-19 Vaccine for Emergency Use in Children 5 through
11 Years of Age.” U.S. Food and Drug Administration, FDA, 29
Oct. 2021, https://www.fda.gov/news-events/press-
announcements/fda-authorizes-pfizer-biontech-covid-19-
vaccine-emergency-use- children-5-through-11-years-age.
Dror, Amiel A., et al. “Vaccine Hesitancy: The next Challenge
in the Fight against COVID- 19.” European Journal of
Epidemiology, 2020, https://doi.org/10.21203/rs.3.rs- 35372/v1.
“Covid-19 Vaccination Demographics in the United
States,National.” Centers for Disease Control and Prevention,
8. Centers for Disease Control and Prevention, 2021,
https://data.cdc.gov/Vaccinations/COVID-19-Vaccination-
Demographics-in-the-United- St/km4m-vcsb/data.
CDC. “Information for Pediatric Healthcare Providers.” Centers
for Disease Control and Prevention, Centers for Disease Control
and Prevention, https://www.cdc.gov/coronavirus/2019-
ncov/hcp/pediatric-hcp.html.
Vaccination measure
Average Ages_12-15_yrs Ages_16-17_yrs Ages_18-
24_yrs Ages_18-29_yrs Ages_25-39_yrs Ages_30-
39_yrs Ages_40-49_yrs Ages_50-64_yrs Ages_65-
74_yrs Ages_75+_yrs Ages_ < 12yrs Ages_ <
18yrs 2468250.6204819279 1773310.0180722892
7477076.915662651 13469317.274096385
19457114.481927712 13464874.123493975
13846150.171686746 25678542.921686746
17009685.015060242 12097490.638554217
56670.063253012049 4298230.7018072288 Median
Ages_12-15_yrs Ages_16-17_yrs Ages_18-24_yrs
Ages_18-29_yrs Ages_25-39_yrs Ages_30-39_yrs
Ages_40-49_yrs Ages_50-64_yrs Ages_65-74_yrs
Ages_75+_yrs Ages_ < 12yrs Ages_ <
18yrs 106061 1823868 8815734 16117987
24044837 16742584 17666655 34308157 22584249.5
15700141 3118.5 1933047.5 Standard Deviation
Ages_12-15_yrs Ages_16-17_yrs Ages_18-24_yrs
Ages_18-29_yrs Ages_25-39_yrs Ages_30-39_yrs
Ages_40-49_yrs Ages_50-64_yrs Ages_65-74_yrs
Ages_75+_yrs Ages_ < 12yrs Ages_ <
18yrs 2823358.6302446662 1621023.6165294982
5814245.1032565795 10053396.586859321
13539535.261377096 9296111.1089917962
9407487.6122821439 17045460.183151565
9851381.7412278894 6504222.6511004353
9. 59388.358080549253 4457456.9074671958 Range
Ages_12-15_yrs Ages_16-17_yrs Ages_18-24_yrs
Ages_18-29_yrs Ages_25-39_yrs Ages_30-39_yrs
Ages_40-49_yrs Ages_50-64_yrs Ages_65-74_yrs
Ages_75+_yrs Ages_ < 12yrs Ages_ <
18yrs 7340399 4189860 15644051 27475522
37568158 25736687 25517438 44700890 26192718
17805678 129146 11659405 Sum Ages_12-15_yrs
Ages_16-17_yrs Ages_18-24_yrs Ages_18-29_yrs
Ages_25-39_yrs Ages_30-39_yrs Ages_40-49_yrs
Ages_50-64_yrs Ages_65-74_yrs Ages_75+_yrs
Ages_ < 12yrs Ages_ < 18yrs
819459206 588738926 2482389536 4471813335
645 9762008 4470338209 4596921857 8525276250
5647215425 4016366892 18814461 1427012593
Research paper (write-up) composition and related notes
• Your project should demonstrate use of all below Data
Analytics concepts.
• You should describe the steps you take, the visuals, your
conclusions and other detail that would
help the reader understand the work completed.
Section / focus area Notes and What is important
I. Research statement
1-2 paragraphs to describe clearly what your paper examines
and why it is important
II. Dataset description
1 paragraph description of the data, including links to the
dataset(s)
III. Applied Data Analytics
Include detailed descriptions of your work in each subsection.
Include a minimum of 2 paragraphs for each section to describe
10. your findings. You will need to:
• Demonstrate applicability and use of key course concepts
• Start with a summary of what analysis you did in this section
III
and of the steps taken in each subsection
• Conduct data transformation, where applicable, and include a
description on what was done and why
1. Data visualization and exploration
Initial data exploration and related visualizations
2. Descriptive Statistical Measures
Applying related concepts to better understand data and
potential
relationships
3. Random Sampling and
Probability Distributions Demonstrate application of related
concepts
4. Sampling and Estimation
Obtain various samples and apply related principles
5. Statistical Inference Using samples, conduct tests related to
your hypothesis and
interpret the results obtained
6. Regression Analysis
Conduct regression analysis, apply a systematic approach to
build your regression model and test your prediction results
IV. Conclusions Summarize your analysis, insights obtained,
existing or potential
results that may falsify your conclusions and suggested next
steps
Team1 Verni Olivia ADA-I ProjecDateAges_<12yrsAges_12-
15_yrsAges_16-17_yrsAges_18-29_yrsAges_25-
39_yrsAges_30-39_yrsAges_40-49_yrsAges_50-
64_yrsAges_65-74_yrsAges_75+_yrsAges_<18yrsAges_18-
24_yrs011/12/21129146734039941898602747552237568158257
36687255174384470089026192718178056781165940515644051
111/11/2112903373324454186503274450143752887325711034
