DISSERTATION RESEARCH: Epidemiology meets symbiosis: modeling symbiont spread through multi-host communities

大多数新的人类传染病是人畜共患的，这意味着引起这些疾病的病原体起源于野生动物和家畜，然后传播给人类。许多感染牲畜和野生动物的病原体也在其他物种中发现。描述病原体在单一野生动物宿主物种内（例如，蝙蝠种群内的狂犬病传播）和不同宿主物种之间（例如，蝙蝠和臭鼬之间的狂犬病传播）传播的数学模型是理解和预测人类、牲畜和野生动物疾病暴发的关键工具。除了用于了解病原体在宿主中的传播之外，这些模型还可以用于了解有益共生体（如生活在更大宿主中的细菌等小生物）的传播，这些共生体有助于而不是伤害宿主。然而，作为这些数学模型基础的基本假设很少得到检验，因为观察动物接触率和疾病在自然界的传播是困难的。这项研究将使用宿主及其共生体的系统来测试现有模型所基于的假设，并将量化这些模型在野生动物种群中预测疾病传播的能力。这项研究的结果最终将导致更好地预测共生传播的数学模型。野生动物种群密度经常随时间和空间的变化而变化。经典的流行病学模型使用两种机制传播函数中的一种来描述宿主密度与病原体传播率之间的关系。第一个假设动物接触率和传播率随宿主密度线性增加（密度相关传播），第二个假设动物接触率和传播率不受宿主密度影响（频率相关传播）。然而，在这些极端之间的非线性关系可能更适合于许多宿主-共生体系统。本研究利用一个实验可处理的多宿主系统——以淡水蜗牛为食的共生环节蠕虫，将：(1)实证量化宿主密度与种内和种间宿主接触率之间的关系；(2)利用所得模型对自然系统中广泛时空尺度的单宿主和多宿主群落的共生传播动态进行预测和测试。对基本模型假设的批判性评估和由此产生的模型预测将导致更好的共生传播预测模型。

Most new infectious diseases in humans are zoonotic, which means the pathogens that cause these diseases originate in wild and domestic animals and then spread to humans. Many pathogens that infect livestock and wildlife species are also found in other species. Mathematical models that describe pathogen transmission within single wildlife host species (e.g., rabies transmission within bat populations) and between different host species (e.g., rabies transmission between bats and skunks) are critical tools for understanding and predicting disease outbreaks in humans, livestock, and wildlife. In addition to being used to understand the spread of pathogens among hosts, these same models can be used to understand the spread of beneficial symbionts (small organisms such as bacteria that live in much larger hosts) that help rather than harm the host. However, the fundamental assumptions that underlie these mathematical models are rarely tested, because observing animal contact rates and spread of disease in nature is difficult. This research will use a system of hosts and their symbionts to test assumptions on which the existing models are built, and will quantify how well those models can predict disease transmission in wildlife populations. The results of this research will ultimately lead to mathematical models that are better at predicting symbiont transmission. Wildlife population densities often vary across space and time. Classic epidemiological models use one of two mechanistic transmission functions to describe the relationship between host density and pathogen transmission rates. The first assumes that animal contact rates and thus transmission rates increase linearly with host density (density-dependent transmission), and the second assumes that animal contact rates and transmission rates are not affected by host density (frequency-dependent transmission). However, nonlinear relationships that fall somewhere between those extremes may be more appropriate in many host-symbiont systems. Using an experimentally tractable multi-host system - symbiotic annelid worms living on freshwater snails - this research will: (1) empirically quantify the relationship between host density and both intra- and inter-specific host contact rates, and (2) use the resulting model to make and test predictions regarding symbiont transmission dynamics in single and multi-host host communities at broad spatial and temporal scales in natural systems. Critical evaluation of fundamental model assumptions and the resulting model predictions will lead to better predictive models of symbiont transmission.