Forecasting New COVID-19 Cases and Deaths Based on an Intelligent Point and Interval System Coupled With Environmental Variables - Qu, Zongxi; Sha, Yongzhong; Xu, Qian; Li, Yutong (Frontiers in Ecology and Evolution, 2022)
The outbreak of COVID-19 has become a global public health event. Effective Covid-19 outbreak trend forecasting is still a complex and challenging issue due to the significant fluctuations and non-stationary inherent in Covid-19 new cases and deaths series. Most previous studies mainly focused on univariate prediction and ignored the uncertainty prediction of COVID-19 pandemic trends, which may lead to insufficient results. Therefore, this paper develops a novel intelligent point and interval multivariate forecasting system that consists distribution function analysis module, intelligent point prediction module, and interval forecasting module. Aimed at the characteristics of the COVID-19 series, eight hybrid models composed of various distribution functions and optimization algorithms are effectively designed in the analysis module to determine the exact distribution of the COVID-19 series. Then, the point prediction module presents a hybrid multivariate model with environmental variables. Finally, interval forecasting is calculated based on distribution functions and point prediction results to obtain uncertainty information for decision making. The new cases and new deaths of Covid-19 were collected from three top affected countries to conduct an empirical study. Empirical results demonstrate that the proposed system can achieve better prediction results than other comparable models and enables the informative and practical quantification of future COVID-19 pandemic trends, which offer more constructive suggestions for governmental administrators and the general public.