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

人类的大多数新传染病都是人畜共患的，这意味着导致这些疾病的病原体起源于野生和家养动物，然后传播到人类。在其他物种中也发现了许多感染牲畜和野生动物物种的病原体。描述单个野生动物寄主物种内的病原体传播(例如，蝙蝠种群内的狂犬病传播)和不同寄主物种之间的病原体传播(例如，蝙蝠和臭鼬之间的狂犬病传播)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。除了被用来理解病原体在宿主之间的传播外，这些相同的模型还可以被用来理解有益共生体(生活在更大宿主中的细菌等小型生物)的传播，这些共生体有助于而不是损害宿主。然而，这些数学模型背后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系统来测试现有模型所依据的假设，并将量化这些模型预测野生动物种群中疾病传播的能力。这项研究的结果最终将导致更好地预测共生体传播的数学模型。野生动物的种群密度往往会在空间和时间上有所不同。经典的流行病学模型使用两个机械传播函数中的一个来描述宿主密度和病原体传播率之间的关系。第一种假设动物接触率和传播率随着宿主密度线性增加(密度依赖的传播)，第二种假设动物接触率和传播率不受宿主密度的影响(频率依赖的传播)。然而，介于这两个极端之间的非线性关系在许多宿主-共生体系统中可能更合适。使用一种实验上易处理的多宿主系统--生活在淡水蜗牛上的共生环节动物蠕虫--这项研究将：(1)经验地量化宿主密度与种内和种间宿主接触率之间的关系，以及(2)使用所得到的模型在自然系统中广泛的空间和时间尺度上对共生体在单宿主和多宿主群落中的传播动态进行预测和检验。对基本模型假设和由此产生的模型预测的严格评估将导致共生体传播的更好的预测模型。

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.