NSF Postdoctoral Fellowship in Biology FY 2013

2013 财年 NSF 生物学博士后奖学金

• 批准号: 1306474
• 负责人: Daniel Streicker
• 金额: $ 14万
• 依托单位: Streicker Daniel G
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1306474&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; FY

Integrating field, molecular and surveillance data to predict and control wildlife zoonosesEndemic and newly emerging infectious diseases of wildlife represent important threats to biodiversity, human health and socio-economic advancement. Management of wildlife diseases is often limited by high system complexity and sparse data. Incorporating molecular sequence data into mechanistic models of disease transmission is one of the most promising avenues to advance understanding of pathogen persistence and thereby improve prospects for control. This research will use vampire bat transmitted rabies to develop a statistical framework that integrates field data from bat colonies in Peru with time series and molecular sequence data from rabies outbreaks in livestock. Analysis of viral sequence data will identify geographic predictors of viral diffusion across landscapes. Data from field surveys and national surveillance programs will inform spatially-explicit transmission models. Transmission trees generated from these models will be compared to observed viral phylogenies for parameter refinement and model selection. Simulation studies will explore the efficacy of alternative culling or oral vaccination plans.Maximizing biological inference from diverse datasets is a frontier in disease ecology that will improve management of a variety of zoonotic diseases. This project will provide a framework to integrate three types of data that are increasingly collected in studies that seek mechanistic understanding of pathogen persistence: longitudinal field surveillance, time series and molecular sequence data. The research will generate a sophisticated understanding of rabies epidemiology in vampire bats, a disease responsible for thousands of domestic animal deaths yearly and increasing human rabies mortality. The tools developed will inform control programs throughout Latin America and constitute a first step towards viral elimination, while expanding scientific capacity in a developing country.

综合野外、分子和监测数据预测和控制野生动物人畜共患病野生动物的地方性和新发传染病对生物多样性、人类健康和社会经济发展构成了重大威胁。野生动物疾病的管理往往受到系统复杂性高和数据稀少的限制。将分子序列数据转化为疾病传播的机制模型是促进对病原体持久性的理解从而改善控制前景的最有希望的途径之一。这项研究将使用吸血蝙蝠传播的狂犬病开发一个统计框架，整合从秘鲁蝙蝠殖民地的现场数据与时间序列和分子序列数据，从狂犬病爆发的牲畜。对病毒序列数据的分析将确定病毒在各地传播的地理预测因子。来自实地调查和国家监测计划的数据将为空间明确的传播模型提供信息。将从这些模型生成的传播树与观察到的病毒遗传进行比较，以进行参数优化和模型选择。模拟研究将探索替代扑杀或口服疫苗接种plannes.Maximizing生物推断从不同的数据集的有效性是一个前沿的疾病生态学，将改善各种人畜共患病的管理。该项目将提供一个框架，以整合在寻求病原体持久性机制理解的研究中越来越多地收集的三种类型的数据：纵向实地监测、时间序列和分子序列数据。这项研究将对吸血蝙蝠的狂犬病流行病学产生复杂的理解，这种疾病每年导致数千只家畜死亡，并增加人类狂犬病死亡率。开发的工具将为整个拉丁美洲的控制方案提供信息，并构成消除病毒的第一步，同时扩大发展中国家的科学能力。