Collaborative Research: Linking Within-Host and Between-Host Infectious Disease Dynamics

合作研究：将宿主内和宿主间传染病动态联系起来

• 批准号: 1515661
• 负责人: Maia Martcheva
• 金额: $ 20.74万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1515661&HistoricalAwards=false
• 关键词: Collaborative; Research; Linking; Within; Host

Infectious diseases, such as malaria and influenza-like illnesses, are leading causes of sickness and mortality among humans and wild and domestic animals. There is considerable data available on the effects of pathogens within a single host individual and the resulting responses of the host immune system, and also for the distribution of the infectious diseases within host populations (for example, the total number of infections and the rates of new infections). Pathogen abundance within a host can rise after infection, and then wane to the point of extinction because of host immune responses. This project will create mathematical models that link such within-host pathogen dynamics to the spread of pathogens between hosts. These models will make use of both sources of data, leading to a better understanding of these infectious diseases and therefore possible strategies for their control. For example, these types of models have shown that HIV medications decrease HIV levels within patients, increasing patient lifespan, which can perversely result in more HIV cases in the population as a whole. Sufficiently wide-spread application of these medications, however, may decrease the number of cases. Using multi-scale data validates these models and allows their application to make predictions in cases where population data is scant. The models that will be developed can be used to assess how the pathogen load within a host affects the number of disease cases and the effect of control measures such as medications and vaccination on the number of cases. Most infectious diseases that still to this day create significant death, particularly among humans, are caused by pathogens that can evolve very quickly; hosts can also evolve in response to these pathogens. Models that link within-host and between-host data are particularly adept for studying host and pathogen evolution, adding a new dimension to our understanding of how to control diseases with pathogens that can change rapidly, such as malaria, hepatitis C, and influenza.The central research objectives of this project are: (1) Develop, analyze and simulate multi-scale mathematical models that link immunology, epidemiology and ecology in vector-borne diseases or diseases involving ecologically interacting species, and that are well grounded in disease epidemiology, ecology and immunology. (2) Develop mathematical techniques to analyze multi-scale models and study disease evolution using these models. Develop numerical techniques to fit such models to data, thus integrating immunological and epidemiological data. (3) Use immuno-epidemiological models to address important questions related to control and evolution of vector-borne diseases and diseases involving ecologically interacting species. In particular, two types of questions will be addressed: (a) How do within-host dynamics affect the population level reproduction number and disease prevalence and what consequences does that have for the control of infectious diseases? (b) How do pathogens and hosts evolve in their within-host interactions to enhance their population level performance?

疟疾和流感样疾病等传染病是人类、野生动物和家畜患病和死亡的主要原因。 关于病原体对单个宿主个体的影响和由此引起的宿主免疫系统的反应，以及传染病在宿主群体中的分布（例如，感染总数和新感染率），有大量数据可用。 宿主体内的病原体丰度在感染后会上升，然后由于宿主的免疫反应而下降到灭绝的程度。 该项目将创建数学模型，将这种宿主内病原体动态与宿主之间的病原体传播联系起来。 这些模型将利用这两种数据来源，从而更好地了解这些传染病，从而制定可能的控制战略。 例如，这些类型的模型表明，艾滋病毒药物降低了患者体内的艾滋病毒水平，延长了患者的寿命，这可能会导致整个人群中更多的艾滋病毒病例。 然而，这些药物的充分广泛应用可能会减少病例数量。 使用多尺度数据验证这些模型，并允许它们的应用程序在人口数据不足的情况下进行预测。 将开发的模型可用于评估宿主体内的病原体负荷如何影响疾病病例数以及药物和疫苗接种等控制措施对病例数的影响。 大多数至今仍造成重大死亡的传染病，特别是在人类中，是由可以非常迅速地进化的病原体引起的;宿主也可以进化以应对这些病原体。 将宿主内和宿主间数据联系起来的模型特别适合于研究宿主和病原体的进化，为我们理解如何控制具有快速变化病原体的疾病（如疟疾、丙型肝炎和流感）增添了新的维度。本项目的主要研究目标是：（1）开发、分析和模拟多尺度数学模型，将病媒传播疾病或涉及生态相互作用物种的疾病的免疫学、流行病学和生态学联系起来，并且在疾病流行病学、生态学和免疫学方面有很好的基础。(2)开发数学技术来分析多尺度模型，并使用这些模型研究疾病演变。发展数字技术，使这些模型与数据相适应，从而整合免疫学和流行病学数据。(3)利用免疫流行病学模型解决与病媒传播疾病和涉及生态相互作用物种的疾病的控制和演变有关的重要问题。具体而言，将讨论两类问题：（a）宿主内部的动态如何影响种群数量、繁殖数量和疾病流行率，以及这对控制传染病有何影响？(b)病原体和宿主如何在宿主内相互作用中进化以增强其种群水平的表现？