DISSERTATION RESEARCH: Scaling within host interactions to epidemic patterns

论文研究：将宿主相互作用扩展到流行病模式

• 批准号: 1601353
• 负责人: Volker Rudolf
• 金额: $ 2.01万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1601353&HistoricalAwards=false
• 关键词: DISSERTATION; RESEARCH; Scaling; within; host

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.

寄生虫调节几乎所有人群的动态，并且绝大多数宿主（包括人）同时被多种寄生虫物种感染。多种寄生虫物种的感染是否会增加宿主种群流行病的风险？这项研究将建立一个通用框架，以预测共同感染如何影响宿主人群的流行病。 结果将有助于预测并发出现的寄生虫的后果，并将告知策略，以帮助遏制威胁野生动植物，动物股票，农作物和人类种群的流行病。该项目将通过支持两个将改善现有数学模型的新实验来扩展博士生的研究和培训。研究人员将通过有关传染病的讲座以及参与实验的设计和结果分析，参与休斯顿公立学校的各种学生。当多个寄生虫感染宿主时，感染顺序可能起重要作用，因为它可以确定传输率和宿主死亡率。到达顺序的影响 - 或优先效应 - 在单个宿主中有充分的文献记载，但很少在经典模型中纳入以预测和理解多寄生虫流行病。该项目的目的是确定（1）感染的相对时机如何改变多寄生虫系统中的流行病，以及多种感染是否会增加流行病结果的变化，以及（2）测试是否可以使用单主持人数据纳入共同感染后果的机械模型来预测这种变化。这些目标将使用实验和模型的组合来实现。第一个实验将检查多寄生虫流行病，并具有多个寄生虫的到达顺序，并双重感染和单独感染的种群。这将确定优先级影响如何改变宿主人群水平的流行模式。将进行一系列单宿主感染实验，以参数化一个预测性流行模型，该模型解释了宿主的感染历史以及该历史如何影响共同感染的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，将测试自然种群中流行病模式的变化，即在考虑宿主的感染历史时，可以从单个宿主数据中预测。该模型的预测能力将与不包括优先级效果的传统模型进行比较。