Collaborative Research: Modeling Complex Dynamics of Host-Parasite Interactions

该项目的具体目标有三个方面。第一个目标是开发评估多种寄主类型的多种寄生虫株/种的感染和混合感染的影响的模型。第二个目标是分析药物治疗(包括宿主的年龄结构)对寄生虫进化的影响。最终目的是探索寄生虫的毒力、繁殖和耐药性在形成寄生虫和宿主种群方面所起的作用，并获得关于寄主生活史和寄生虫异质性对寄生虫入侵和持久性的影响的有用见解。这些模型是为了反映寄主-寄生虫系统的生物学复杂性而开发的，并将使用非线性动力系统理论中的工具进行分析。此外，还将启动旨在测试理论预测并为模型提供生物学相关参数的补充性实证研究。这些研究将为各种因素如何影响疾病在自然系统中的建立和传播提供独特的见解。为了实现项目目标，数学家和生物学家将结合他们的专业知识来产生更相关和更适用的模型。理论模型对于疾病生态学、病原体及其宿主的共同进化和寄生虫传播动力学等学科的科学发展至关重要。不幸的是，由于寄生虫与宿主的复杂相互作用，这些学科的理论进展变得复杂起来。由于可获得的相关数据有限，这些领域的进展进一步放缓。然而，如果我们要开发为公共卫生政策提供洞察力的模型，就必须应对这些挑战。该项目的目标是将数学和经验数据结合起来，通过开发更准确地描述种群动态和评估疾病控制措施的现实模型来了解复杂的宿主-寄生虫相互作用。通过明确地将模型与适当的实验工作联系起来，这项研究将极大地促进我们对宿主-寄生虫共同进化和疾病流行病学的认识和理解。将分析性研究和经验性研究结合起来的重点是确定基于模型的工具的潜力，使实地工作人员和政策制定者了解公共卫生决策可能产生的生态和流行病学后果。

The specific objectives of this project are threefold. The first objective is to develop models for assessing the impact of infection and co-infection of multiple strains/species of parasites in multiple host types. The second objective is to analyze the impact of drug treatment (including the age-structure of hosts) on parasite evolution. The final objective is to explore the roles that parasite virulence, reproduction, and drug resistance play in shaping parasite and host populations, and to obtain useful insights into the effects of host life-history and parasite heterogeneity on parasite invasion and persistence. These models are developed to reflect the biological complexity of host-parasite systems and will be analyzed using tools in nonlinear dynamical systems theory. In addition, complementary empirical studies that are designed to test theoretical predictions and provide biologically relevant parameters for the models will be initiated. These studies will provide unique insights into how various factors impact the establishment and spread of disease in natural systems. To achieve the project objectives, mathematicians and biologists will combine their expertise to generate more relevant and applicable models.Theoretical models are essential to the scientific growth of disciplines such as disease ecology, co-evolution of pathogens and their hosts, and parasite transmission dynamics. Unfortunately, theoretical advances in these disciplines are complicated by the complex interactions of parasites with their hosts. Advances in these areas are further slowed by the limited availability of relevant data. Yet these challenges must be met if we are to develop models that provide insights for public health policy. The goal of this project is to combine mathematics and empirical data to understand complex host-parasite interactions by developing realistic models that more accurately describe population dynamics and evaluate disease control measures. By explicitly linking the models with appropriate experimental work, this research will significantly advance our knowledge and understanding of host-parasite co-evolution and disease epidemiology. The focus on the integration of analytical and empirical studies is to identify the potential for model-based tools to inform field workers and policymakers of the likely ecological and epidemiological consequences of public health decisions.