Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America.
Lamont-Doherty Earth Observatory, Columbia University, New York, NY, United States of America.
PLoS Comput Biol. 2019 Feb 28;15(2):e1006783. doi: 10.1371/journal.pcbi.1006783. eCollection 2019 Feb.
Process-based models have been used to simulate and forecast a number of nonlinear dynamical systems, including influenza and other infectious diseases. In this work, we evaluate the effects of model initial condition error and stochastic fluctuation on forecast accuracy in a compartmental model of influenza transmission. These two types of errors are found to have qualitatively similar growth patterns during model integration, indicating that dynamic error growth, regardless of source, is a dominant component of forecast inaccuracy. We therefore examine the nonlinear growth of model initial error and compute the fastest growing directions using singular vector analysis. Using this information, we generate perturbations in an ensemble forecast system of influenza to obtain more optimal ensemble spread. In retrospective forecasts of historical outbreaks for 95 US cities from 2003 to 2014, this approach improves short-term forecast of incidence over the next one to four weeks.
基于过程的模型已被用于模拟和预测许多非线性动力系统，包括流感和其他传染病。在这项工作中，我们评估了模型初始条件误差和随机波动对流感传播的隔室模型预测精度的影响。这两种类型的误差在模型积分过程中表现出相似的增长模式，这表明动态误差增长（无论其来源如何）是预测不准确的主要因素。因此，我们研究了模型初始误差的非线性增长，并使用奇异向量分析计算了最快增长方向。利用这些信息，我们在流感的集合预报系统中生成了扰动，以获得更优的集合扩展。在对 2003 年至 2014 年 95 个美国城市的历史疫情进行的回顾性预测中，该方法提高了对未来一到四周发病率的短期预测。
