Quantifying Error Growth to Improve Infectious Disease Forecast Accuracy

量化误差增长以提高传染病预测准确性

• 批准号: 10424587
• 负责人: JEFFREY L SHAMAN
• 金额: $ 64.91万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-09 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10424587
• 关键词: 2019-nCoV; Assimilations; Behavior; Biological Models; Breeding; Calibration; Characteristics; Climate; Clinics and Hospitals; Combinatorial Optimization; Communicable Diseases; Commuting; Complex; Coronavirus; Country; Data; Decision Making; Dengue; Diagnosis; Disease; Disease Outbreaks; Disease Surveillance; Ebola; Endemic Diseases; Epidemic; Error Sources; Future; Geography; Growth; Healthcare; Hospital Planning; Incidence; Influenza; International; Lead; Location; Mathematics; Mediating; Methods; Modeling; Outcome; Patients; Process; Public Health; Recurrent disease; Research; Site; Source; Structure; System; Time; Travel; Uncertainty; Weather; West Nile virus; Work; base; design; improved; infectious disease model; insight; medical countermeasure; models and simulation; network models; novel; pandemic influenza; pathogen; respiratory virus; response; seasonal influenza; simulation; sound; surveillance data; surveillance network; transmission process; vector

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.

项目概要/摘要 在过去的十年中，传染病预测取得了长足的进步。使用源自的方法 动态建模、统计推断和数值天气预报、预报系统 专为流感、SARS-CoV-2、登革热和埃博拉等疾病而开发。这些系统已经产生了 对未来流行病结果的概率预测，具有可量化的准确性和长达 3 个月的交付时间， 在某些情况下，已经投入使用以实时提供预测。此类预报信息 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，并根据患者激增情况分配医疗用品。需要持续的研究 进一步提高这些疾病预测的准确性，以便基于此的决策和行动 这些信息更有动力。为此，至关重要的是确定传染性错误的来源 疾病预测得到了更好的理解，预测过程中误差的增长被量化，并且方法 开发这些方法是为了控制和优化误差增长，从而提高预测准确性。此举的目的 该项目旨在利用已用于理解和量化天气误差增长的方法 预测模型并提高天气预报准确性，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）量化各种误差内的非线性增长 传染病预测模型，然后开发方法来优化期间的误差增长 预测，从而提高预测准确性；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；量化误差增长可以是 使用最佳扰动方法结合观察和数据同化进行利用 方法，生成更校准的集合预测，从而产生更准确的概率 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，以便了解当观察缺失时不确定性如何传播并识别 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）开发模型进行预测 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传播；我们 假设可以更准确地预测这些疾病的国内和国际传播 利用网络模型结构的系统。该项目的研究结果将提高对错误的理解 预测模型的增长，提高传染病预测的准确性，提供信息 监测实践，并能够更准确地预测疾病的传播。