Enhancing COVID-19 Case Forecasting in the United States: A Comparative Analysis of ARIMA, SARIMA, and RNN Models with Grid Search Optimization

Publication date: Mar 03, 2024

The COVID-19 pandemic has resulted in a substantial number of fatalities in the United States since its onset in January 2020. In an effort to mitigate the spread of this highly infectious disease, a range of measures, including social distancing, mask-wearing, lockdowns, and vaccination campaigns, have been implemented. However, despite these extensive efforts, the persistent transmission of the virus can be attributed to a combination of vaccine hesitancy among certain individuals and the emergence of new viral strains. To effectively manage the ongoing pandemic, healthcare providers and government officials rely on infectious disease modeling to anticipate and secure the necessary resources. Accurate short-term case number forecasting is of paramount importance for healthcare systems. Since the beginning of the pandemic, numerous models have been employed to forecast the number of confirmed cases. In this study, we undertake a comparative analysis of six time-series techniques: Simple Moving Average (SMA), Exponentially Weighted Moving Average (EWMA), Holt-Winters Double Exponential Smoothing Additive (HWDESA), Autoregressive Integrated Moving Average (ARIMA), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Recurrent Neural Network (RNN), with regard to their modeling and forecasting capabilities. SMA, EWMA, and HWDESA were employed for predictive modeling, while the ARIMA, SARIMA, and RNN models were utilized for case number forecasting. A comprehensive grid search was carried out to determine the optimal parameter combinations for both the ARIMA and SARIMA models. Our research findings demonstrate that the Holt-Winters Double Exponential model outperforms both the Exponentially Weighted Moving Average and Simple Moving Average in predicting the number of cases. On the other hand, the RNN model surpasses conventional time-series models such as ARIMA and SARIMA in terms of its forecasting accuracy. The finding of this study emphasizes the importance of accurately predicting the number of COVID-19 cases, given the substantial loss of lives caused by both the virus itself and the societal responses to it. Equipping healthcare managers with precise tools like Recurrent Neural Networks (RNNs) can enable them to forecast future cases more accurately and enhance their preparedness for effective response.

Concepts Keywords
Coronavirus Arima
Economics Average
Meticulous Covid
Terns Ewma
March
Medrxiv
Modeling
Models
Moving
Neural
Preprint
Sarima
Seasonal
Series
Sma

Semantics

Type Source Name
disease MESH COVID-19
disease MESH infectious disease
pathway REACTOME Infectious disease
disease VO vaccination
disease VO vaccine
disease VO time
disease VO Optaflu
disease VO effective
disease VO combination vaccine
disease VO effectiveness
disease IDO intervention
disease MESH infection
disease MESH dengue
disease MESH death
drug DRUGBANK Spinosad
disease IDO country
drug DRUGBANK Aspartame
drug DRUGBANK Fenamole
disease MESH aids
disease VO BT1
disease IDO process
drug DRUGBANK Pentaerythritol tetranitrate
disease IDO history
disease MESH Metabolic Syndrome

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