Spatio-temporal model to forecast COVID-19 confirmed cases in high-density areas of Malaysia - Abd Rahman, Nur Haizum; Muhammad Yusof, Saidatul Nurfarahin; Che Ilias, Iszuanie Syafidza; Gopal, Kathiresan; Yaacob, Hannuun; Mohammad Sham, Noraishah (Penerbit UTM Press, 2024)
The coronavirus 2019 disease has spread across the world. The number ofcoronaviruses 2019 (COVID-19) cases throughout Malaysia is high in the densely populated state of Selangor. In assisting the early preventive measures, this study utilises time series methods to model and forecast the number of daily positive cases in three Selangor districts: Petaling, Hulu Langat, and Klang. Specifically, the study compares the effectiveness of the Autoregressive Integrated Moving Average (ARIMA), a univariate model and the Generalized Space-Time autoregressive integrated (GSTARI), a multivariate model. For the GSTARI model, uniform and inverse distance weights represent the relationship between locations. The analysed data are from January to August 2021, and the lowest root mean square error (RMSE) is chosen as the best model. The results show GSTARI (1,1) with both spatial weights outperformed ARIMA (0,1,1) in Petaling and Klang but not in Hulu Langat. However, the average RMSE values show that the most accurate and effective for forecasting the number of daily confirmed positive cases in Selangor is using GSTARI. In conclusion, by utilising advanced time series methods such as spatial analysis, this study provides important insights into forecasting trends of infectiousdiseases like COVID-19 and can help in early preventive measures.