Forecasting COVID19 parameters using time-series: KSA, USA, Spain, and Brazil comparative case study

Souad Larabi-Marie-Sainte; Sawsan Alhalawani; Sara Shaheen; Khaled Mohamad Almustafa; Tanzila Saba; Fatima Nayer Khan; Amjad Rehman

doi:10.1016/j.heliyon.2022.e09578

Forecasting COVID19 parameters using time-series: KSA, USA, Spain, and Brazil comparative case study

Heliyon. 2022 Jun;8(6):e09578. doi: 10.1016/j.heliyon.2022.e09578. Epub 2022 Jun 2.

Authors

Souad Larabi-Marie-Sainte¹, Sawsan Alhalawani¹, Sara Shaheen¹, Khaled Mohamad Almustafa², Tanzila Saba³, Fatima Nayer Khan², Amjad Rehman³

Affiliations

¹ Department of Computer Science, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
² Department of Information Sciences, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia.
³ Artificial Intelligence Data Analytics (AIDA) Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 12435, Saudi Arabia.

Abstract

Many countries are suffering from the COVID19 pandemic. The number of confirmed cases, recovered, and deaths are of concern to the countries having a high number of infected patients. Forecasting these parameters is a crucial way to control the spread of the disease and struggle with the pandemic. This study aimed at forecasting the number of cases and deaths in KSA using time-series and well-known statistical forecasting techniques including Exponential Smoothing and Linear Regression. The study is extended to forecast the number of cases in the main countries such that the US, Spain, and Brazil (having a large number of contamination) to validate the proposed models (Drift, SES, Holt, and ETS). The forecast results were validated using four evaluation measures. The results showed that the proposed ETS (resp. Drift) model is efficient to forecast the number of cases (resp. deaths). The comparison study, using the number of cases in KSA, showed that ETS (with RMSE reaching 18.44) outperforms the state-of-the art studies (with RMSE equal to 107.54). The proposed forecasting model can be used as a benchmark to tackle this pandemic in any country.

Keywords: COVID-19; Drift; Exponential smoothing; Forecasting; Holt; Linear regression; Time-series.