Quantifying Model Uncertainty for Forecasting the Spread of Infectious Diseases

量化预测传染病传播的模型不确定性

• 批准号: 8269980
• 负责人: Sara Del Valle
• 金额: $ 66.97万
• 依托单位: LOS ALAMOS NAT SECTY-LOS ALAMOS NAT LAB
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8269980
• 关键词: Behavior; Behavior Therapy; Behavioral; Behavioral Model; Communicable Diseases; Communities; Complex; Computer Simulation; Computer software; Contact Tracing; Coupled; Data; Databases; Development; Disease; Disease Outbreaks; Disease Progression; Drug Formulations; Effectiveness; Epidemic; Epidemiology; Foundations; Future; Goals; Human; Incidence; Influenza A Virus, H1N1 Subtype; Institute of Medicine (U.S.); Intervention; Intervention Studies; Measures; Methodology; Modeling; Paper; Pattern; Policy Maker; Population; Prevalence; Preventive; Principal Investigator; Public Health; Publishing; Quarantine; Relative (related person); Reliance; Research Infrastructure; Resolution; Science; Severe Acute Respiratory Syndrome; Simulate; System; Testing; Uncertainty; Vaccination; base; behavior change; computer code; cost; epidemiological model; fighting; improved; mathematical model; models and simulation; novel strategies; pandemic disease; predictive modeling; response; simulation; social; tool; verification and validation; virtual

DESCRIPTION (provided by applicant): The primary goal of our proposal is to enhance the nation's public health response capability by quantifying agent-based model uncertainty and the impact of behavioral modification on the spread of infectious diseases. Our goal is to improve the understanding of the impact of emergent behavior on the accuracy and applicability of predictive models of disease spread. We will evaluate the implications of uncertainty in human and population behavioral response to a pandemic in mathematical model formulations. This foundational understanding will help improve all existing epidemiological models thereby potentiating the ability of public health practitioners and policy-makers to effectively manage a burgeoning epidemic regardless of the tool being used. We will leverage existing epidemiologic and behavioral simulation infrastructure to develop new mathematical approaches to incorporate different types of diseases and behavioral changes alone and in combination with other intervention strategies. We will validate the models and quantify sensitivity of computational models to parameters, and known disease spread patterns. These models will be constructed for use in estimating prevalence and incidence and will allow us to compare systematically the relative effects of preventive measures, such as behavioral changes, isolation, contact tracing, quarantine, and vaccination. First, we will develop novel approaches to characterize emergent behavior and extend the mathematical foundation and software infrastructure for modeling behavior changes in response to an epidemic. Secondly, we will quantify the epidemic progression uncertainty caused by the distribution of behavioral responses. These behavioral models will be implemented and validated in an existing high-fidelity agent-based activity simulator model. Finally, we will disseminate these advances so they can be useful, and used, in other epidemiological simulations. RELEVANCE: The data for an ongoing epidemic is sparse, inexact, and often just unavailable. Therefore, quantifying parameter and computational uncertainties is crucial for forecasting the impact of disease spread. We cannot assume impact of the uncertain parameters is negligible; especially when decisions based on the model will impact the lives of countless people. One of the fundamental limitations of the current models is in how well they capture changes in human behavior in response to an ongoing endemic.

描述（由申请人提供）：我们提案的主要目标是通过量化基于主体的模型不确定性以及行为改变对传染病传播的影响来增强国家的公共卫生应对能力。我们的目标是提高对突发行为对疾病传播预测模型的准确性和适用性影响的理解。我们将评估数学模型公式中人类和群体对大流行的行为反应的不确定性的影响。这一基本认识将有助于改进所有现有的流行病学模型，从而增强公共卫生从业者和政策制定者有效管理迅速蔓延的流行病的能力，无论使用何种工具。我们将利用现有的流行病学和行为模拟基础设施来开发新的数学方法，以单独或与其他干预策略相结合来纳入不同类型的疾病和行为变化。我们将验证模型并量化计算模型对参数和已知疾病传播模式的敏感性。这些模型将用于估计患病率和发病率，并使我们能够系统地比较预防措施的相对效果，例如行为改变、隔离、接触者追踪、检疫和疫苗接种。首先，我们将开发新的方法来表征突发行为，并扩展数学基础和软件基础设施，以对应对流行病的行为变化进行建模。其次，我们将量化行为反应分布引起的流行病进展不确定性。这些行为模型将在现有的基于代理的高保真活动模拟器模型中实施和验证。最后，我们将传播这些进展，以便它们在其他流行病学模拟中有用和使用。 相关性：当前流行病的数据稀疏、不准确，而且往往无法获得。因此，量化参数和计算不确定性对于预测疾病传播的影响至关重要。我们不能假设不确定参数的影响可以忽略不计；尤其是当基于模型的决策将影响无数人的生活时。当前模型的基本局限性之一在于它们如何很好地捕捉人类行为的变化以应对持续的流行病。