Utilizing an Ensemble Kalman Filter and SEIRV Model to Estimate the Vaccination Rate of Measles
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open in viewerEpidemiological models are a mathematical representation of the transmission of certain diseases. Studying these models can help predict the spread of these diseases. This graduate project analyzes the Susceptible - Exposed - Infectious - Recovered - Vaccinated (SEIRV) model as it relates to measles and explores the contribution of each parameter within the model, specifically the vaccination rate, to help predict and prevent the spread of measles. Using an Ensemble Kalman Filter (EnKF), each compartment population in the SEIRV model can be estimated, along with unknown parameters. In this work, the vaccination rate of measles is estimated using the EnKF in order to approximate the trend of vaccination rates over years and predict a measles outbreak. This is done using simulated data motivated by the potential of applying this method to an example with real data.
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- etd-109201
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- 2023
- Date created
- 2023-05-05
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- etd-109201
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- 2023-06-01
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Grad_Project_Final__2_.pdf | Public | Download |
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