I501_Final_Proposal_Sample
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Indiana University, Purdue University, Indianapolis *
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Course
B530
Subject
Medicine
Date
Dec 6, 2023
Type
docx
Pages
4
Uploaded by AdmiralMantisMaster473
Predicting unwillingness to receive COVID-19 vaccines and examining its contributing
factors
Team members:
************, ************, ************, ************,
************, ************
Introduction:
The development of vaccines from Pfizer, AstraZeneca, Moderna, Novavex, Johnson and
Johnson, etc. gives us hope to end the pandemic caused by COVID-19. However, we hear many
daily news reports that some health workers are refusing vaccines (CBSNews, Cerulo) or anti-
vaccine activists’ protests are happening. (NYTimes, Fernandez) According to the
House Pulse
Survey done by CDC on January 6
th
, 24% of the population were hesitant to receive the vaccine
by responding “probably not” (14%) or “definitely not” (9.5%) on the question asking their
willingness to get the vaccine. Of those who were hesitant to receive the vaccine, 53% responded
“I am concerned about possible side effects of a COVID vaccine”, 19% responded “I don’t know
if a COVID vaccine works”, and 9.85% “I don’t believe I need a COVID vaccine.
Aim:
We aim to find out the contributing factors of people’s unwillingness to get the COVID-19
vaccines and to examine the possibility of predicting people’s unlikelihood of getting COVID-19
vaccines based on those factors.
Purpose:
The purpose of the study is to explain why certain populations are not willing to get the COVID-
19 vaccines. According to Rasmussen, population-level herd immunity is essential to control
SARS-CoV-2 in the long run and getting vaccinated is the key to reach the herd immunity
threshold of SARS-CoV-2. (Rasmussen, 2020) Therefore, we would like to examine the factors
below (House Pulse Survey, CDC) and predict people’s unwillingness to get the vaccine in
relation to these factors so that organizations can identify where people’s uncertainties or
concerns are originating from and come up with ideas of how to approach those behaviors.
Age, gender, hispanic origin, race, education, marital status, total number of people under 18-
year-old in household, Recent job loss, expected household job loss, employment status, kind of
work, difficulty with expenses, health insurance, prescription mental health, mental Health
services, mental health not get, Home owned, Pandemic children education status, income.
Methodology:
Type of study: Quantitative Research (Descriptive Statistics and Inferential Statistics)
Data collection: CDC House Pulse Survey in Phase 3 (Jan 3- 18) who took the survey in
California
(
https://www.census.gov/programs-surveys/household-pulse-
survey/datasets.html
)
Data storage: CSV files
Data extraction, description: Finding statistical significance of correlation factors,
normality test
Data analysis: Model development, performance analysis using Python, data visualization
using Python Seaborn, Matplotlib, Plotly.
Hypothesis:
Null Hypothesis: The factors we examined do not show associations with the
participants’ willingness to get COVID-19 vaccines and those factors cannot predict their
unwillingness to get the COVID-19 vaccine.
Alternate Hypothesis: The factors we examined show associations with the participants’
willingness to get COVID-19 vaccines and those factors can predict their unwillingness
to get the COVID-19 vaccine.
Deliverables:
1)
Descriptive Statistics:
i)
Data Extraction using SQL
ii)
Normality Test using Python
a)
Data Visualization of Normality Test Results using Python Seaborn and
Matplotlib (seaborn pairplots, histogram, heatmap)
iii)
Unsupervised learning (Clustering) to classify people into group
a)
Data Visualization of Clustering using Python Matplotlib
2)
Model development: Binary classification model, risk prediction model
3)
Performance Analysis
i)
Evaluating Binary Classification
a)
Logistic Regression: Visualization using Python
b)
Support Vector Machine: Visualization using Python
c)
Naives Bayes Classifier: Visualization using Python
ii)
Evaluating risks by risk score of people not willing to get the vaccine
Results:
1)
The alternate hypothesis had statistical significance that the examined or (certain
factors of examined) factors contributed to people’s hesitancy of getting vaccinated.
2)
We were able to predict people’s unwillingness to get vaccinated based upon those
contributing factors and risk scores
Team members Responsibility:
Team Member
Responsibilities
************
Data extraction using SQL
Normality test using Python
************
Binary classification model development
Clustering analysis
Data visualization of clustering
************
Evaluating binary classification (Logistic Regression)
using Python
Visualization of linear regression model using Python
Matplotlib
************
Data visualization of normality test results using Python
Seaborn and Matplotlib (seaborn pairplots, histogram,
heatmap)
Evaluating binary classification (Support Vector Machine)
using Python
Visualization of SVM using Python
************
Evaluating binary classification (Naives Bayes Classfier)
using Python
Visualization of Naives Bayes classifier using Python
Final project report
************
Project proposal
Risk prediction model development
Evaluating risks by risk score using Python
Timeline:
Date
Tasks
3/15
Data extraction, normality test
3/22
Data visualization of normality test
Binary classification model development
3/29
Risk prediction model development
Logistic regression evaluation
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4/5
Support vector machine evaluation
4/12
Naives Bayes evaluation
4/19
Clustering
Risk score evaluation
4/26-4/30
Final project report
References:
Cerullo, M. (2021, Feb 25, 2021). Many health care workers are refusing to get a COVID-19
vaccine.
https://www.cbsnews.com/news/covid-vaccine-health-care-worker-reluctance/
Fernandez, M. (2021, Feb 6). Anti-Vaccine Activists Emboldened in California.
The New York
Times
.
https://www.nytimes.com/2021/02/06/us/california-covid-vaccine.html
Rasmussen, A. L. (2020). Vaccination Is the Only Acceptable Path to Herd Immunity.
Med
,
1
(1),
Pages 21-23. https://doi.org/https://doi.org/10.1016/j.medj.2020.12.004.