DISSERTATION RESEARCH: Scaling within host interactions to epidemic patterns

寄生虫控制着几乎所有种群的动态，绝大多数宿主（包括人类）同时被多种寄生虫感染。多种寄生虫的感染是否会增加宿主人群中流行的风险？这项研究将建立一个总体框架，预测共同感染如何影响宿主人群中的流行病。结果将有助于预测同时出现的寄生虫的后果，并将为帮助遏制威胁野生动物、动物种群、农作物和人口的流行病的战略提供信息。该项目将通过支持两个将改进现有数学模型的新实验来扩展博士生的研究和培训。研究人员将通过传染病讲座、参与实验设计和结果数据分析，吸引休斯敦公立学校各种各样的学生。当多种寄生虫感染宿主时，感染顺序可能起着重要作用，因为它可以决定传播率和宿主死亡率。到达顺序的影响——或优先效应——在单个宿主中有很好的记录，但很少被纳入预测和理解多寄生虫流行的经典模型中。该项目的目标是确定(1)感染的相对时间如何改变多寄生虫系统中的流行，以及多重感染是否会增加流行结果的变化，以及(2)测试这种变化是否可以用结合单宿主数据的共感染后果的机制模型来预测。这些目标将通过实验和模型的结合来实现。第一个实验将检查具有多个寄生虫到达顺序和双重感染和单一感染人群的多寄生虫流行病。这将决定优先效应如何改变东道国人口一级的流行病模式。将进行一系列单宿主感染实验，以参数化预测流行病模型，该模型说明了宿主的感染历史以及这种历史如何影响共感染寄生虫和宿主之间的相互作用。将模型预测与经验流行病进行比较，将检验在考虑宿主感染史时，是否可以根据个体宿主数据预测自然种群中流行病模式的变化。该模型的预测能力将与不包括优先效应的传统模型进行比较。

Parasites regulate the dynamics of almost all populations, and the vast majority of hosts (including humans) are infected by multiple parasite species at the same time. Do infections by multiple parasite species increase the risk of epidemics in host populations? This research will establish a general framework that predicts how co-infections affect epidemics in host populations. Results will help predict the consequences of concurrent emerging parasites and will inform strategies to help curb epidemics that threaten wildlife, animal stocks, agricultural crops, and human populations. The project will extend the research and training of a doctoral student by supporting two new experiments that will improve existing mathematical models. The researchers will engage a diverse range of students in the Houston Public Schools through lectures on infectious disease and participation in the design of experiments and analysis of resulting data. When multiple parasites infect hosts, the order of infection likely plays an important role because it can determine transmission rates and host mortality. The effects of arrival order - or priority effects - are well documented within single hosts but rarely incorporated in classical models to predict and understand multi-parasite epidemics. The goal of this project is to determine (1) how the relative timing of infections alters epidemics in multi-parasite systems and whether multiple infections increase variation in outcome of epidemics, and (2) test whether this variation can be predicted with mechanistic models that incorporate the consequences of co-infection from single-host data. These goals will be accomplished using a combination of experiments and models. The first experiment will examine multi-parasite epidemics with multiple arrival orders of parasites and doubly infected and singly infected populations. This will determine how priority effects alter epidemic patterns at the host population level. A series of single host infection experiments will be conducted to parameterize a predictive epidemic model that accounts for the infection history of hosts and how this history influences interactions among co-infecting parasites and hosts. Comparing model predictions to empirical epidemics will test whether variation in epidemics patterns in natural populations can be predicted from individual host data when accounting for the infection history of hosts. The predictive power of this model will be compared to traditional models that do not include priority effects.