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
Abstract :
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.
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.
