INTERPRETING THE FUTURE OF COVID-19 WITH STATISTICAL FORECASTING MODELS
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Keywords
Statistical Models, ForecastingForecasting, ARIMA model, SETAR model, ARCH model, GARCH model, Sars-Cov2.
Abstract
Sars-Cov2 is a deadly virus effected millions of peoples globally. Time series forecasting helps us to identify and plan things properly related to any particular disease or viruses. This is daily data of Covid-19 and researcher intended to find out best statistical model.
Objective: To evaluate best statistical model which forecast covid-19 data related to new cases in subcontinents of Pakistan.
Methods: This was an analytical observational design with daily data of new cases of COVID-19 among sub-continents of Pakistan. Data was imported from world health organization website. In this study statistical models applied were AR, MA, ARIMA, SETAR model by using threshold regression, ARCH effect, Simple GARCH model and Component GARCH model. Forecasting models used were AIC, MAPE, MAE and RMSE. Eviews version 12.0 used for data analysis.
Results: A total of 1146 observations for each country were taken for analysis. AR and MA model observed that Azerbaijan was significant at (1,0,1) model with AIC= 14.55, SBIC=14.57, HQC=14.56 and adjusted R2=0.911. Bangladesh was significant at (1,0,2) model with AIC=15.55, SBIC=15.57, HQC=15.56 and adjusted R2=0.958. Similarly, China was significant at (1,0,2) model, India was significant at (1,0,1) model, Iran found significant at (1,0,2) model. However, Pakistan, Sri Lanka and Kazakhstan were statistically significant at (1,0,1) model respectively.
Conclusion: These comprehensive long-term results showed best forecast models among different statistical models like AR, MA, ARIMA, SETAR, GARCH and component GARCH models with statistically significant findings. ARIMA and GARCH models showed best fit among all models to forecast pandemic new cases. Due to a globally controlled environment WHO announced that after 8th May 2023 global health emergency was ended and no further cases was reported therefore, we have reported the data before the ending emergency by WHO.
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Muhammad Faisal Fahim
Bahria University Health Sciences Campus Karachi, Sailor St Cantonment, Karachi 74400, Sindh, Pakistan.
Tayyab Raza Fraz
Department of Statistics, University of Karachi, Circular Road, Karachi, Sindh, Pakistan.
Ali Asghar Mirjat
Bahria University Health Sciences Campus Karachi, Sailor St Cantonment, Karachi 74400, Sindh, Pakistan.
Anzila Meer
Department of Otorhinolaryngology (ORL), Universiti sains Malaysia USM kubang kerian kelantan Malaysia.
Muhammad Gulzada
Bahria University Health Sciences Campus Karachi, Sailor St Cantonment, Karachi 74400, Sindh, Pakistan.
Hasnain Pasha
Department of Optometry, POB Eye Hospital Karachi, C-15 Munawar Chowrangi Road, Block 12 Gulistan-e-Johar, Karachi, Karachi City, Sindh 74500.
Amjad Ali
Bahria University Health Sciences Campus Karachi, Sailor St Cantonment, Karachi 74400, Sindh, Pakistan.
Albert John
Bahria University Health Sciences Campus Karachi, Sailor St Cantonment, Karachi 74400, Sindh, Pakistan.