RAISE: IHBEM: Behavioral Heterogeneity and Uncertainty in Epidemiological Models

在这个项目中，现有的流行病学模型将进一步发展，以应对两个基本挑战：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，以减少传染病的发病率、死亡率和经济影响。通过解决两个问题，可以进一步提高其效力。首先，人们对风险的认知、社交网络的规模、每天接触的数量、参与公共卫生措施的意愿以及对错误和错误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主要挑战，而人类行为是确保流行病学模型预测准确性的核心。其次，不确定性弥漫在所有的流行病学模型中。所有模型都依赖于输入参数，如无症状感染的比例，这些参数不能确切知道，但由于未知参数在看似合理的范围内变化，模型预测可能会发生巨大变化。忽视关键输入参数中的这种内在不确定性，可能会导致对模型的预测过于自信，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。该项目由数学和自然科学局(MPS)的数学科学司(DMS)和社会、行为和经济科学局(SBE)的社会和经济科学司(SES)共同资助。在这个项目中，一系列统计和机器学习方法被用于公开可用的数据集，以拟合与流行病学模型相关的人类行为的参数和非参数模型。先进的技术被开发和使用来从接触者追踪数据中引出人类的行为，对缺失的数据和此类数据固有的选择偏差进行调整。由此产生的分析为优化方法提供了投入，这些方法旨在优化政策干预，同时明确考虑到异质性和不确定性。量身定制的优化方法在短时间尺度(天)上进行优化，通过对描绘社会网络的图形模型的优化，以及在接触结构模型上的特征值优化问题来最小化传播速度，以及在较长时间尺度(几周或几个月)上进行优化，其中考虑到对干预措施的行为反应，如疲劳。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.