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Description

With the onset of the COVID-19 pandemic, it has become of increasing interest to both monitor and predict the growth of its infection rates. In order to analyze the accuracy of epidemiological prediction, we consider two different models for prediction, the Susceptible Infected and Removed (SIR), and Autoregressive Integrated Moving Average (ARIMA) models. Using a dataset of Clark County COVID-19 infections, we create various ARIMA and SIR models that attempt to predict the progression of COVID-19 infections whilst comparing these predictions to the dataset. We observed that the ARIMA model performed more accurately overall, having a much lower Root Mean Squared Error than its counterpart.

Publication Date

Spring 2021

Language

English

Keywords

Infection; Epidemiology; Machine learning; COVID-19

Disciplines

Applied Mathematics | Epidemiology | Numerical Analysis and Computation

File Format

pdf

File Size

222 KB

Comments

Faculty Mentor: Monika Neda, Ph.D.

Modeling COVID-19 Infection Rates using SIR and ARIMA Models


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