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: 10278807
• 负责人: JEFFREY L SHAMAN
• 金额: $ 66.53万
• 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/10278807
• 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; Structural Models; 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.

项目摘要/摘要 在过去的十年中，传染病的预测已大大提高。使用从 动态建模，统计推断和数值天气预测，预测系统已经 为流感，SARS-COV-2，登革热和埃博拉病毒等疾病开发。这些系统已经生成 概率预测未来的流行病结果具有可量化的准确性和长达3个月的交货时间， 在某些情况下，已经进行了运营以实时提供预测。这样的预测信息 可以用来帮助管理医疗对策的时间和分配，计划医院和诊所 人员配备，并分配医疗保健供应，以预期患者潮流。需要进行持续的研究 进一步提高这些疾病预测的准确性，以便基于 这些信息是更有动力的。为此，至关重要的是传染性的错误来源 疾病的预测可以更好地理解，预测期间误差的增长是量化的，并且方法是 为了控制和优化该错误增长，以提高预测准确性。这个目的 项目是利用已用于理解和量化天气错误增长的方法 预测模型并提高天气预测准确性，并将这些方法应用于感染力 疾病预测系统。具体而言，我们将：1）量化多样性内的非线性误差的生长 传染病预测模型，然后开发方法以优化该错误在 预测，从而提高了预测准确性；我们假设疾病中最快的增长模式 可以使用单数矢量分析（SVA）确定预测模型；然后可以量化错误增长 使用最佳扰动方法利用，结合观测和数据同化 方法，生成更校准的整体预测，产生更准确的概率 预测； 2）将SVA和最佳扰动方法应用于最近经过验证的空间显式模型 流感以了解丢失观察时的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 用于识别改进的，最佳的疾病监测网络； 3）开发模型以预测 并预测了国际流感和SARS-COV-2的持续传播；在这里，我们将开发多种多样 国家通过空间解释的网络化种群模型，能够准确模拟和预测 季节性流感和SARS-COV-2在国家之间和国家之间的传播和传播；我们 假设这些疾病的国内和国际蔓延可以更准确地预测 利用网络模型结构的系统。该项目的发现将改善对错误的理解 预测模型的增长，提高操作感染疾病预测的准确性，告知 监视实践，并可以更准确地预测疾病的传播。