M.S. (Master of Science)
Department of Statistics and Actuarial Science
The novel Coronavirus, known as COVID-19 is a highly contagious and transmissible infectious disease that has taken a toll throughout the entire world for over a year. The inner workings and long term effects of COVID-19 continue to be misunderstood. While COVID-19 has impacted all countries tremendously, Latin American countries and specifically Colombia have been impacted significantly by the virus. This thesis investigates the potential to forecast COVID-19 cases and deaths using Time Series Analysis methods and models for the South American country of Colombia. Time series analysis on Colombian COVID-19 data begins with data processing on a data set consisting of COVID-19 data for all countries with reported COVID-19 case and death data. The aforementioned data set then goes through subsetting in order to obtain COVID-19 data for the country of Colombia. Model diagnostics and analysis for cases and deaths due to COVID-19 are explored using ARIMA models, and Simple Exponential Smoothing models. Models are then used to forecast cases and deaths for the next one, two, six and twelve months, respectively. Model comparison between ARIMA and SES models is performed to assess forecast accuracy for each model. Model selection measures such as AIC, BIC are utilized, while MAPE, RMSE, MAE, and MPE are utilized in comparing forecasting accuracy between the models.
Jackson-Sagredo, Andrea, "A Time Series Analysis Approach to Forecasting COVID-19 Cases and Deaths: An Analysis of COVID-19 Data in Colombia" (2021). Graduate Research Theses & Dissertations. 7221.
Northern Illinois University
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