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.

在当今高度联系的世界中，对流行病的预防，预测和控制对于全球卫生，经济生产力和地缘政治稳定至关重要。在过去的二十年中，许多传染病暴发表明需要流行病学建模。他们还揭示了现有科学技术的缺点，以准确预测流行动力学并制定有效的控制策略。该项目将建立一种新的有效仿真方法，使得可以评估稀有但高度结果的事件。它将用于识别有关分类病毒相互作用的结构的决定性风险因素，这些因素可以促进大量流行病暴发。一个有据可查的例子是超级宣传的事件，在Covid-19大流行中发挥了重要作用。该研究将集中在类似于COVID-19和HIV的疾病模型上，作为原型病例。对流行病学过程的深入理解和模型将用于设计和分析有效的预防策略，目的是为公众和健康政策决策者提供更可靠的指导，挽救生命和资源，从传统上进行传统的疾病的动态，以确定群体的划分为基础，以确定范围的群体，并将其划分为确定的阶段，并将其定向。动力学。经典示例是确定性的SIR和SIS模型。这是对现实的强烈简化，在很大程度上忽略了整个人群中的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质。两者都对流行病爆发的早期阶段的动态有决定性的影响，并且需要纳入以实现可靠的预测。马尔可夫链蒙特卡洛方法可以采样更现实的随机剂动力学，但无法有效评估导致极少数结果事件的前提。该项目将通过一种新的数值技术来应对这一挑战，该技术使人们能够在适当的约束下有效地对现实模型的重要但罕见的流行轨迹进行采样。这项研究将重新注意罕见事件在大暴发的起源中的关键作用，包括接触网络中瓶颈的组合和疾病动力学的随机性质。基于新方法的风险因素分析将为有关爆发前提，信息流和控制策略的疾病扩散中的尖端问题提供答案。这种方法将为预防和控制流行病的研究开放新的途径。该项目由数学科学划分（DMS）的数学生物学计划共同资助，该项目在数学和物理科学局（MPS）（MPS）以及人类网络和数据科学计划（HNDS）的行为和认知科幻局（BCARATE SCISICES）（BCARATE SCIIS）（BCCIS）（BCCI）（BCCI）（BCCI）（BCC）领导（BCC）领导，以实施BCC的领导，以实施BCC的行为，以实施BCC的行为（HNDS）。 （SBE）。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响审查标准的评估来支持的。