Quantifying Error Growth to Improve Infectious Disease Forecast Accuracy

项目总结/摘要 在过去十年中，传染病预测取得了很大进展。使用源自 动力学模式、统计推断和数值天气预报，预报系统已经 针对流感、SARS-CoV-2、登革热和埃博拉等疾病开发的疫苗。这些系统产生了 以可量化的准确性和长达3个月的准备时间对未来流行病结果进行概率预测， 并且在某些情况下，已经被操作化以提供真实的预报。这样的预测信息 可用于帮助管理医疗对策的时间安排和分配，规划医院和诊所 人员配备，并在预期患者激增的情况下分配医疗用品。需要进行研究， 进一步提高这些疾病预测的准确性，以便根据 这些信息的动机更加合理。为此，至关重要的是，传染性疾病的错误来源 更好地理解疾病预测，预测过程中误差的增长是量化的，方法 是为了控制和优化误差增长，以提高预测精度。的目的 项目是利用已采用的方法来了解和量化天气误差增长 预测模型，提高天气预报的准确性，并将这些方法应用于传染病 疾病预测系统具体来说，我们将：1）量化误差的非线性增长的多样性， 传染病预测模型，然后开发方法来优化误差增长， 预测，从而提高预测的准确性;我们假设疾病中增长最快的模式 可以使用奇异向量分析（SVA）来识别预测模型;然后可以量化误差增长， 利用最佳扰动方法，结合观测和数据同化 方法，以生成一个更校准的集合预报，产生更准确的概率 预测; 2）将SVA和最优扰动方法应用于最近验证的空间显式模型， 流感，以了解当观测缺失时不确定性如何传播，并确定 哪些位置对于整个网络的准确预测至关重要;我们假设这些发现可以 用于确定改进的、更优化的疾病监测网络; 3）开发预测模型 并预测流感和SARS-CoV-2在国际上的持续传播;在这里，我们将开发多- 国家空间明确的网络化集合种群模型，能够准确模拟和预测 季节性流感和SARS-CoV-2在国家内部和国家之间的传播和扩散;我们 假设这些疾病的国内和国家间传播可以更准确地预测， 使用网络模型结构的系统。该项目的发现将提高对错误的理解 预测模型的增长，提高业务传染病预测的准确性， 监测做法，并能够更准确地预测疾病的传播。

PROJECT SUMMARY/ABSTRACT Over the last decade, infectious disease forecasting has advanced considerably. Using methods derived from dynamic modeling, statistical inference and numerical weather prediction, forecast systems have been developed for diseases such as influenza, SARS-CoV-2, dengue and Ebola. These systems have generated probabilistic forecasts of future epidemic outcomes with quantifiable accuracy and lead times up to 3 months, and in some instances, have been operationalized to deliver forecasts in real time. Such forecast information can be used to help manage the timing and distribution of medical countermeasures, to plan hospital and clinic staffing, and to allocate healthcare supplies in anticipation of patient surges. Ongoing research is needed to further improve the accuracy of these disease forecasts so that the decisions and actions that are based on this information are more soundly motivated. To this end, it is vital that the sources of error in infectious disease forecasts are better understood, that the growth of error during forecast is quantified, and that methods are developed to control and optimize that error growth in order to improve forecast accuracy. The aim of this project is to leverage methods that have been employed to understand and quantify error growth in weather forecasting models and to improve weather forecasting accuracy, and to apply these methods to infectious disease forecasting systems. Specifically, we will: 1) quantify the nonlinear growth of error within a diversity of infectious disease forecasting models and then develop methods to optimize that error growth during forecasting, thus improving forecast accuracy; we hypothesize that the fastest growing mode within disease forecasting models can be identified using singular vector analysis (SVA); quantified error growth can then be exploited using optimal perturbation methods, in conjunction with observations and data assimilation approaches, to generate a more calibrated ensemble forecast that produces more accurate probabilistic predictions; 2) apply SVA and optimal perturbation methods to a recently validated, spatially explicit model of influenza in order to understand how uncertainty propagates when observations are missing and to identify which locations are critical for accurate forecasting throughout the network; we hypothesize these findings can be used to identify improved, more optimal disease surveillance networks; and 3) develop models to forecast and project the continued spread of influenza and SARS-CoV-2 internationally; here, we will develop multi- country spatially-explicit networked metapopulation models capable of accurate simulation and forecasting of the transmission and spread of seasonal influenza and SARS-CoV-2 within and between countries; we hypothesize that the intra- and inter-country spread of these diseases can be forecast more accurately with systems that utilize network model structures. The findings from this project will improve understanding of error growth in forecast models, improve the accuracy of operational infectious disease forecasting, inform surveillance practices, and enable more accurate forecast of the spread of disease.