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Predictions of Changes in Child Immunization Rates Using an Automated Approach: USA

Mannion, Niall (2020) Predictions of Changes in Child Immunization Rates Using an Automated Approach: USA. Masters thesis, Dublin, National College of Ireland.

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Immunizations help to save millions of lives each year from infectious diseases. Declining rates
of immunizations caused by concerns about their safety and sociodemographic/socioeconomic
factors have resulted in a rise in deaths from infectious disease. Past studies in the area of
immunization hesitancy have focused on generating advanced statistical and machine learning
techniques that non-coders may not be able to use. This research designed a platform using R
Markdown that analysed immunization related content from Twitter and Google Trends,
generated data mining models using patient survey data to predict whether or not a child will
receive immunizations, and predicted trends in immunization coverage with time series
forecasts. The work generated a visual dashboard of the online content, and the SuperLearner
data mining model was the most accurate model in predicting child’s immunization status, with
76% accuracy, sensitivity of 30.01% and specificity of 80%. Timeseries forecasts had a mean
absolute error of 6.83. The simple automated platform could be used by healthcare workers
and public health officials to model trends in immunization coverage and anticipate changes in
immunization rates to prevent deaths from infectious disease.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
R Medicine > RA Public aspects of medicine
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Dan English
Date Deposited: 18 Jan 2021 14:16
Last Modified: 18 Jan 2021 14:16

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