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

• 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

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.

疾病建模和公共卫生目前面临的挑战是了解从宿主内到宿主间感染规模的病原体动力学。感染后的病毒和免疫反应动力学影响到向其他宿主的传播，并反馈到整个人群的免疫力，所有这些都会影响疾病的总体负担和爆发的轨迹。例如，登革病毒（DENV）负荷和宿主免疫力是错综复杂的;宿主中预先存在的抗体的某些水平实际上可能会增加不同血清型继发感染的严重性。 更好地了解免疫学和流行病学动态的耦合对于DENV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突出了这一点。 该项目开发了新颖的多尺度建模框架，包括动态分析，计算方法和数据拟合，用于破译跨尺度的疾病结局。 考虑了三个案例研究：（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）中病原体持久性和宿主免疫力的反馈;以及（iii）媒介病毒动力学和接种物（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，并提供对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的跨学科科学领域的高级培训。感染尺度的相互联系对描述复杂病毒至关重要，也带来了重要的数学/计算挑战。虽然多尺度模型已被应用于传染病，一个主要的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统一与个人感染动力学检查感染的多个菌株，减弱/增强免疫力，和载体的能力，沿着疾病控制。一类新的抗体结构的免疫流行病学微分方程模型将制定描述变量宿主免疫和感染轨迹之间的连接。 正在开发的方法包括创新的平衡，稳定性和持久性分析，沿着多尺度模拟方法。 此外，将通过拟合宿主/病媒内数据和流行病学数据来利用多尺度设置，为模型提供信息和验证。 对于DENV，将与生物学家合作，结合从感染期间的病毒免疫动力学到流行病发病率的几个数据集，以提高模型的能力和预测。 该项目由数学科学部（DMS）的数学生物学项目和刺激竞争性研究的既定项目（EPSCoR）共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Modeling the uncertainty in epidemiological models through interval analysis considering actual data from two municipalities in Colombia affected by dengue

考虑哥伦比亚两个受登革热影响城市的实际数据，通过区间分析对流行病学模型的不确定性进行建模

• DOI: 10.1016/j.apm.2022.07.006
• 发表时间: 2022
• 期刊: Applied Mathematical Modelling
• 影响因子: 5
• 作者: Lizarralde-Bejarano, Diana Paola;Gulbudak, Hayriye;Kearfott, Ralph Baker;Puerta-Yepes, María Eugenia
• 通讯作者: Puerta-Yepes, María Eugenia

A delay model for persistent viral infections in replicating cells

复制细胞中持续病毒感染的延迟模型

• DOI: 10.1007/s00285-021-01612-3
• 发表时间: 2021
• 期刊: Journal of Mathematical Biology
• 影响因子: 1.9
• 作者: Gulbudak, Hayriye;Salceanu, Paul L.;Wolkowicz, Gail S.
• 通讯作者: Wolkowicz, Gail S.