Applied Data Analytics project

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 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.

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Fig. 1. “Covid Vaccine Eligibility by Date in the United States” by Olivia Verni

(Dooling, MD et al.), (Office of the Commissioner)

Fig. 2. “Number of Fully Vaccinated People in the U.S. as of November 12, 2021 by Age Bracket”

by Olivia Verni

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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 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

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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

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

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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 vaccination 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”

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, 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 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