A Simple Statistical Model for Forecasting COVID-19 Infections with Application to South and Southeast Asian Countries

Nowadays, countries worldwide have been facing crises due to the epidemic of coronavirus disease 2019 (COVID-19). This study aimed to construct a model for forecasting COVID-19 infected cases and deaths using the natural cubic spline function. The data used in this study were obtained from publicly available databases updated daily and located on GitHub run by Microsoft. The model fits the data remarkably well, with r-squared values of 0.997, 0.981, 0.992, 0.975, 0.995, 0.957, 0.973, 0.939, 0.989, 0.881, 0.943, and 0.610 in India, Pakistan, Bangladesh, Indonesia, Singapore, Nepal, Australia, Malaysia, Thailand, Sri Lanka, and Vietnam, respectively. The model generates daily change forecasts that indicate when intervention is required. Furthermore, this model is routinely applied to all such regions globally and can be extended to accommodate additional predictors such as environmental and demographic variables.