Achterberg Massimo A, Prasse Bastian, Ma Long, Trajanovski Stojan, Kitsak Maksim, Van Mieghem Piet
Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, P.O. Box 5031, 2600 GA Delft, The Netherlands.
Microsoft Inc., 2 Kingdom St, London W2 6BD, United Kingdom.
Int J Forecast. 2022 Apr-Jun;38(2):489-504. doi: 10.1016/j.ijforecast.2020.10.001. Epub 2020 Oct 9.
Researchers from various scientific disciplines have attempted to forecast the spread of coronavirus disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the algorithms that we evaluated, the original NIPA performed best at forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.
来自不同科学学科的研究人员试图预测2019冠状病毒病（COVID-19）的传播情况。提出的疫情预测方法从基本的曲线拟合方法、交通交互模型到机器学习方法不等。如果我们将所有这些方法结合起来，就得到了基于网络推理的预测算法（NIPA）。在本文中，我们分析了一系列不同的COVID-19预测算法，包括NIPA的几种改进版本。在我们评估的算法中，原始的NIPA在预测COVID-19在中国湖北和荷兰的传播情况方面表现最佳。特别是，我们表明基于网络的预测优于任何其他预测算法。
