RAISE: IHBEM: Behavioral Heterogeneity and Uncertainty in Epidemiological Models

在这个项目中，现有的流行病学模型进一步发展，以解决两个基本的挑战：异质性和不确定性。 流行病学模型是数学和计算工具，可以帮助指导公共卫生决策，以减轻传染病的发病率，死亡率和经济影响。可通过解决两个问题进一步提高其效力。首先，人们对风险的看法、社交网络规模、每天接触的次数、参与公共卫生措施的意愿以及对错误信息和虚假信息的接受程度各不相同。这种异质性是准确描述人类行为的一个主要挑战，这对于确保流行病学模型的预测准确性至关重要。其次，不确定性弥漫在所有流行病学建模中。所有模型都依赖于输入参数，例如无症状感染的比例，这些参数无法确定，但由于未知参数在合理范围内变化，模型预测可能会发生巨大变化。忽略关键输入参数中的这种内在不确定性可能导致对模型的过度自信的预测，进而导致不考虑适当范围的潜在结果的糟糕决策。这一模式的开发是与州和联邦一级的公共卫生官员组织合作进行的，以确保相关性和实用性。 该项目由数学和物理科学局（MPS）数学科学处（DMS）和社会、行为和经济科学局（SBE）社会和经济科学处（SES）共同资助。一系列统计和机器学习方法被用于公开可用的数据集，以拟合参数和非参数。与流行病学模型相关的人类行为的参数模型。先进的技术被开发出来并用于从接触追踪数据中引出人类行为，调整这些数据中固有的缺失数据和选择偏差。由此产生的分析为优化方法提供了输入，这些方法旨在优化政策干预措施，同时明确考虑异质性和不确定性。定制优化方法通过优化描述社交网络的图模型和通过最小化扩散率的接触结构模型上的特征值优化问题，在短时间尺度（天）和较长时间尺度上进行优化（数周或数月），考虑到对干预措施的行为反应，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

In this project, existing epidemiological models are developed further to address two fundamental challenges: heterogeneity and uncertainty. Epidemiological models are mathematical and computational tools that can help guide public-health decisions to mitigate morbidity, mortality, and economic impacts of communicable diseases. Their effectiveness could be further improved by addressing two issues. First, people differ from one another in their perceptions of risk, social network size, number of contacts per day, willingness to engage with public-health measures, and in their receptivity to mis- and dis-information. This heterogeneity is a major challenge in accurately characterizing human behavior that is central to ensuring prediction accuracy in epidemiological models. Second, uncertainty pervades all epidemiological modeling. All models rely on input parameters, such as the fraction of infections that are asymptomatic, that cannot be known with certainty, yet model predictions can vary dramatically as the unknown parameters vary over plausible ranges. Ignoring this intrinsic uncertainty in key input parameters can lead to overly confident predictions of models and, in turn, to poor decisions that do not consider an appropriate range of potential outcomes. This model development is carried out working with organizations of public health officials at state and federal levels to ensure relevance and practicality. This project is funded jointly by the Division of Mathematical Sciences (DMS) in the Directorate of Mathematical and Physical Sciences (MPS) and the Division of Social and Economic Sciences (SES) in the Directorate of Social, Behavioral, and Economic Sciences (SBE).In this project, a range of statistical and machine-learning methods are employed on publicly available data sets to fit parametric and non-parametric models of human behaviors relevant to epidemiological models. Advanced techniques are developed and used to elicit human behavior from contact-tracing data, adjusting for the missing data and selection bias that is inherent in such data. The resulting analyses provide input into optimization methods that are designed to optimize policy interventions while explicitly accounting for heterogeneity and uncertainty. Tailored optimization methods optimize over both short time scales (days), through optimization of graph models that depict social networks and through eigenvalue optimization problems on contact-structure models that minimize rates of spread, and over longer time scales (weeks or months), that take into account behavioral response to interventions, such as fatigue.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.