Time Series Analysis and Forecasting of COVID-19 Trends in Coffee County, Tennessee, United States

Authors : Cyril Neba C.; Gerard Shu F.; Gillian Nsuh; Philip Amouda A.; Adrian Neba F.; Aderonke Adebisi; P. Kibet.; F.Webnda

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://tinyurl.com/bdcpe7es

Scribd : https://tinyurl.com/3648ka7a

DOI : https://doi.org/10.5281/zenodo.10007394

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has had a profound impact globally, including in the United States and Coffee County, Tennessee. This research project delves into the multifaceted effects of the pandemic on public health, the economy, and society. We employ time series analysis and forecasting methods to gain insights into the trajectory of COVID-19 cases specifically within Coffee County, Tennessee. The United States has witnessed significant repercussions from the COVID-19 pandemic, including public health crises, economic disruptions, and healthcare system strains. Vulnerable populations have been disproportionately affected, leading to disparities in health outcomes. Mental health challenges have also emerged. Accurate forecasting of COVID-19 cases is crucial for informed decision-making. Disease forecasting relies on time series models to analyze historical data and predict future trends. We discuss various modeling approaches, including epidemiological models, data- driven methods, hybrid models, and statistical time series models. These models play a vital role in public health planning and resource allocation. We employ ARIMA, AR, MA, Holt's Exponential Smoothing, and GARCH models to analyze the time series data of COVID-19 cases in Coffee County. The selection of the best model is based on goodness-of-fit indicators, specifically the AIC and BIC. Lower AIC and BIC values are favored as they indicate better model fit. The dataset for this research project was sourced from the Tennessee Department of Health and spans from 12/03/2020 to 12/11/2022. It comprises records of all Tennessee counties, including variables such as date, total cases, new cases, total confirmed, new confirmed, total probable, and more. Our analysis focuses on Coffee County, emphasizing County, Date, and Total cases. Among the models considered, the GARCH model proves to be the most suitable for forecasting COVID-19 cases in Coffee County, Tennessee. This conclusion is drawn from the model's lowest AIC values compared to ARIMA and Holt's Exponential Smoothing. Additionally, the GARCH model's residuals exhibit a distribution closer to normalcy. Hence, for this specific time series data, the GARCH model outperforms ARIMA, AR, MA, and Holt's Exponential Smoothing in terms of predictive accuracy and goodness of fit.

Keywords : COVID-19, Time Series Analysis, Disease Forecasting, ARIMA, AR, MA, Holt's Exponential Smoothing, GARCH, AIC, BIC.


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31 - December - 2023

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