Risk Factor Analysis and Dynamic Response for Epidemics in Heterogeneous Populations

异质人群流行病危险因素分析及动态应对

• 批准号: 2344576
• 负责人: Thomas Barthel
• 金额: $ 50万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2344576&HistoricalAwards=false
• 关键词: Risk; Factor; Analysis; Dynamic; Response

In today's highly connected world, the prevention, prediction, and control of epidemics is of paramount importance for global health, economic productivity, and geopolitical stability. Numerous infectious disease outbreaks over the past two decades have demonstrated the need for epidemiological modeling. They also revealed shortcomings of existing scientific techniques to accurately predict epidemic dynamics and to devise effective control strategies. This project will establish a new efficient simulation method that makes it possible to assess rare but highly consequential events. It will be used to identify decisive risk factors concerning the fabric of virus-spreading interactions that can facilitate large epidemic outbreaks. A well-documented example are superspreading events that played an important role in the COVID-19 pandemic. The investigations will be focused on models for diseases similar to COVID-19 and HIV as archetypal cases. The improved understanding and models of epidemiological processes will be used to devise and analyze efficient preventive strategies with the goal of providing more reliable guidance for the general public and health-policy decision makers, saving lives and resources.Traditionally, the dynamics of infectious diseases are studied on the basis of deterministic compartmental models, where the population is divided into large groups, and deterministic differential equations for the group sizes are employed to investigate disease dynamics. Classical examples are the deterministic SIR and SIS models. This is a strong simplification of reality that ignores to a large extent the heterogeneity in contact patterns and biomedically relevant attributes across the population as well as the stochastic nature of infection processes. Both have a decisive impact on the dynamics at the early stages of epidemic outbreaks and need to be incorporated to enable reliable predictions. Markov-chain Monte Carlo methods can sample more realistic stochastic agent-based dynamics, but cannot efficiently assess the preconditions leading to rare consequential events. The project will address this challenge with a new numerical technique that allows one to efficiently sample important but rare epidemic trajectories of realistic models under suitable constraints. The research will renew attention on the crucial role of rare events in the genesis of large outbreaks, including combinations of bottlenecks in contact networks and the stochastic nature of the disease dynamics. Risk-factor analysis based on the new method will provide answers to cutting-edge questions in disease diffusion concerning outbreak preconditions, information flow, and control strategies. This approach will open new avenues for research on the prevention and control of epidemics.This project is jointly funded by the Mathematical Biology program of the Division of Mathematical Sciences (DMS) in the Directorate for Mathematical and Physical Sciences (MPS) and the Human Networks and Data Science program (HNDS) of the Division of Behavioral and Cognitive Sciences (BCS) in the Directorate for Social, Behavioral and Economic Sciences (SBE).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.

在当今高度互联的世界中，流行病的预防、预测和控制对于全球健康、经济生产力和地缘政治稳定至关重要。过去二十年中无数传染病的爆发证明了流行病学模型的必要性。他们还揭示了现有科学技术在准确预测流行病动态和制定有效控制策略方面的缺陷。该项目将建立一种新的有效模拟方法，使评估罕见但影响重大的事件成为可能。它将用于确定与病毒传播相互作用结构相关的决定性风险因素，从而促进大规模流行病的爆发。一个有据可查的例子是在 COVID-19 大流行中发挥重要作用的超级传播事件。调查将重点关注类似于 COVID-19 和 HIV 的疾病模型作为原型病例。对流行病学过程的更好的理解和模型将用于设计和分析有效的预防策略，目的是为公众和卫生政策决策者提供更可靠的指导，拯救生命和资源。传统上，传染病的动态是基于确定性区室模型进行研究，其中将人口分为大组，并采用组大小的确定性微分方程来研究疾病动态。经典的例子是确定性 SIR 和 SIS 模型。这是对现实的极大简化，在很大程度上忽略了人群中接触模式和生物医学相关属性的异质性以及感染过程的随机性。两者都对流行病爆发早期阶段的动态产生决定性影响，需要将其纳入其中以实现可靠的预测。马尔可夫链蒙特卡罗方法可以对更现实的基于随机主体的动力学进行采样，但无法有效评估导致罕见后果事件的先决条件。该项目将通过一种新的数值技术来应对这一挑战，该技术允许人们在适当的约束下有效地对现实模型的重要但罕见的流行病轨迹进行采样。该研究将重新关注罕见事件在大规模疫情爆发中的关键作用，包括接触网络瓶颈和疾病动态的随机性的组合。基于新方法的风险因素分析将为疾病传播中有关暴发先决条件、信息流和控制策略的前沿问题提供答案。这种方法将为流行病预防和控制的研究开辟新的途径。该项目由数学和物理科学理事会（MPS）数学科学部（DMS）的数学生物学项目和人类网络联合资助社会、行为和经济科学理事会 (SBE) 行为和认知科学部 (BCS) 的数据科学项目 (HNDS)。该奖项反映了 NSF 的法定使命，并具有通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。