Modeling Across-Scale Feedback of Pathogen Virulence, Host Immunity, and Disease Control

病原体毒力、宿主免疫和疾病控制的跨尺度反馈建模

基本信息

批准号：
1951759
负责人：
Hayriye Gulbudak
金额：
$ 23.99万
依托单位：
University of Louisiana at Lafayette
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1951759&HistoricalAwards=false
关键词：
Modeling Across Scale Feedback Pathogen

项目摘要

A current challenge for disease modeling and public health is to understand pathogen dynamics across infection scales from within-host to between-host. Viral and immune response kinetics upon infection impact transmission to other hosts and feedback into population-wide immunity, all of which influence the overall disease burden and trajectory of an outbreak. For example, dengue virus (DENV) burden and host immunity are intricately linked; certain levels of pre-existent antibodies in a host may actually enhance severity of secondary infection with a distinct serotype. A better understanding of the coupled immunological and epidemiological dynamics is critical for control strategies against DENV, highlighted by recent debate over whether vaccination may increase severe dengue infection. This project develops novel multi-scale modeling frameworks with dynamical analysis, computational methods, and data fitting for deciphering disease outcomes across scales. Three case studies are considered: (i) the role of pre-existent antibodies on DENV severity and vaccination; (ii) feedbacks in pathogen persistence and host immunity in Foot and Mouth Disease Virus (FMDV); and (iii) effect of in-vector viral kinetics and inoculum (infection dose) on vector-borne disease epidemics and control. By combining these studies, across-scale feedbacks will be assessed for their influence on disease burden and host immunity, and to provide insights on disease control and dynamics. This work will provide undergraduate and graduate students with advanced training in this emerging interdisciplinary scientific field.The interconnection of infection scales, vital for describing complex viruses, poses important mathematical/computational challenges. While multi-scale models have been applied to infectious diseases, a major limitation has been lack of bidirectional dependence of epidemiological and immunological scales. In this research, population-wide epidemic models are unified with individual infection dynamics to examine infection by multiple strains, waning/boosting of immunity, and vector competence, along with disease control. A new class of antibody-structured immuno-epidemiological differential equation models will be formulated to depict connections between variable host immunity and infection trajectories. Methods under development include innovative equilibrium, stability, and persistence analysis, along with multi-scale simulation approaches. Moreover, the multi-scale setting will be utilized by fitting within-host/vector and epidemiological data to inform and validate the models. For DENV, several datasets, ranging from virus-immune dynamics during infection to epidemic incidence, will be combined to improve power and prediction of the models, in collaboration with biologists. This project is jointly funded by the Mathematical Biology Program of the Division of Mathematical Sciences (DMS) and the Established Program to Stimulate Competitive Research (EPSCoR).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.

当前疾病建模和公共卫生面临的挑战是了解从宿主内部到宿主间感染范围内的病原体动态。感染时的病毒和免疫反应动力学会影响向其他宿主的传播以及对人群免疫力的反馈，所有这些都会影响总体疾病负担和爆发轨迹。例如，登革热病毒（DENV）负担和宿主免疫力有着错综复杂的联系；宿主体内预先存在的一定水平的抗体实际上可能会增强不同血清型继发感染的严重程度。更好地了解免疫学和流行病学的耦合动态对于预防登革热病毒的控制策略至关重要，最近关于疫苗接种是否会增加严重登革热感染的争论凸显了这一点。该项目开发了新颖的多尺度建模框架，具有动态分析、计算方法和数据拟合，以破译跨尺度的疾病结果。考虑三个案例研究：(i) 预先存在的抗体对 DENV 严重程度和疫苗接种的作用； (ii) 口蹄疫病毒（FMDV）病原体持久性和宿主免疫力的反馈； (iii) 媒介内病毒动力学和接种量（感染剂量）对媒介传播疾病流行和控制的影响。通过结合这些研究，将评估跨尺度反馈对疾病负担和宿主免疫力的影响，并提供有关疾病控制和动态的见解。这项工作将为本科生和研究生提供这一新兴跨学科科学领域的高级培训。感染尺度的互连对于描述复杂病毒至关重要，也带来了重要的数学/计算挑战。虽然多尺度模型已应用于传染病，但主要限制是缺乏流行病学和免疫学尺度的双向依赖性。在这项研究中，人口范围内的流行病模型与个体感染动态相结合，以检查多种菌株的感染、免疫力的减弱/增强、媒介能力以及疾病控制。将制定一类新的抗体结构免疫流行病学微分方程模型来描述可变宿主免疫与感染轨迹之间的联系。正在开发的方法包括创新的平衡、稳定性和持久性分析，以及多尺度模拟方法。此外，将通过拟合宿主/媒介和流行病学数据来利用多尺度设置来通知和验证模型。对于 DENV，将与生物学家合作，将多个数据集（从感染期间的病毒免疫动态到流行病发生率）结合起来，以提高模型的能力和预测能力。该项目由数学科学部数学生物学计划 (DMS) 和刺激竞争性研究既定计划 (EPSCoR) 联合资助。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。