Forecasting COVID-19 Number of Cases by Implementing ARIMA and SARIMA with Grid Search in United States

Abstract

COVID-19 has surged in the United States since January 2020. Since then, social distancing and lockdown have helped many people to avoid infectious diseases. However, this did not help the upswing of the number of cases after the lockdown was finished. Modeling the infectious disease can help the health care providers and governors to plan ahead for obtain the needed resources. In this manner, precise short-term determining of the number of cases can be imperative to the healthcare system. Many models have been used since the pandemic has started. In this paper we will compare couple of time series models like Simple Moving Average, Exponentially Weighted Moving Average, Holt-Winters Double Exponential Smoothing Additive, ARIMA, and SARIMA. Two models that have been used to predict the number of cases are ARIMA and SARIMA. A grid search has been implemented to select the best combination of the parameters for both models. Results show that in the case of modeling, the Holt-Winters Double Exponential model outperforms Exponentially Weighted Moving Average and Simple Moving Average while forecasting ARIMA outperforms SARIMA.

Predictive Healthcare Analysis of Pakistan’s COVID-19 Pandemic Using Data Mining and Time Series Modelling

“…In this paper, ARIMA and SARIMA were selected for prediction, and the optimal model parameters for each were found using a grid search. According to the findings, ARIMA performs better than SARIMA in predicting, whereas the Holt-Winters Double Exponential model surpasses the Exponentially Weighted Moving Average and Simple Moving Average [4]. This paper aimed to assess how well the ARIMA model predicted the spread of COVID-19, which the World Health Organization designated a global pandemic in March 2020 after infecting over 4 million people and killing over 300,000 by early May 2020.…”

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

“…In such a way, the pandemic could be monitored, controlled, and properly managed. Many studies demonstrated that among the epidemiological models, the ARIMA model showed the desired precision in predicting the number of cases and fatalities with minimum error[ 10 , 18 ].…”

“…Many other research groups confirmed the effectiveness and usefulness of the ARIMA model for COVID-19 pandemic forecasting[ 10 - 12 ].…”

Comparative analysis of Gated Recurrent Units (GRU), long Short-Term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends

“… This study reported that NNAR model as the best model for forecasting COVID-19 in Turkey. Saina et al [31] 3 2 3 (3,2) (2,0) (3,2) NA NA NA Holt-Winter Exponential smoothing additive (HWESA) model. They reported that HWESA model outperformed ARIMA and SARIMA models.…”