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

疟疾和流感样疾病等传染病是人类以及野生动物和家畜患病和死亡的主要原因。关于病原体在单个宿主个体内的影响和由此引起的宿主免疫系统的反应，以及传染病在宿主种群内的分布（例如，感染总数和新感染率），已有相当多的数据。在感染后，宿主内的病原体丰度会上升，然后由于宿主的免疫反应而下降到灭绝的程度。该项目将建立数学模型，将寄主内病原体动态与寄主间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策略。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出现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的数量。使用多尺度数据验证了这些模型，并允许它们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。大多数直到今天仍然造成大量死亡的传染病，特别是在人类中，都是由可以迅速进化的病原体引起的；宿主也可以进化以应对这些病原体。连接宿主内部和宿主之间数据的模型特别适合研究宿主和病原体的进化，为我们了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）增加了一个新的维度。本项目的主要研究目标是：(1)建立、分析和模拟媒介传播疾病或涉及生态相互作用物种疾病的免疫学、流行病学和生态学的多尺度数学模型，该模型以疾病流行病学、生态学和免疫学为基础。(2)发展数学技术来分析多尺度模型，并利用这些模型研究疾病演变。发展数值技术，使这些模型适合数据，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媒介传播疾病和涉及生态相互作用物种的疾病的控制和进化有关的重要问题。具体而言，将讨论两类问题：(a)宿主内动态如何影响人口水平、繁殖数量和疾病流行，这对控制传染病有什么影响？(b)病原体和宿主如何在宿主内部相互作用中进化，以提高它们在种群水平上的表现？

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?