Collaborative Research:CIBR:VectorByte: A Global Informatics Platform for studying the Ecology of Vector-Borne Diseases

合作研究：CIBR：VectorByte：研究媒介传播疾病生态学的全球信息学平台

• 批准号: 2016264
• 负责人: Leah Johnson
• 金额: $ 89.99万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2016264&HistoricalAwards=false
• 关键词: Collaborative; Research; CIBR; VectorByte; Global

A wide variety of terrestrial plant and animal pathogens have evolved transmission cycles that require vectors, typically arthropods, that transmit the pathogen when it feeds on a host. These vector-borne diseases (VBDs) represent a serious threat to human, animal, and plant health as well as negatively impacting economic welfare worldwide. For example, approximately a third of the human population is at risk for infections transmitted by mosquitoes alone, and vectors transmit many important diseases of plants and livestock. Yearly, VBDs account for 17% of human infectious diseases and billions of dollars in crop and livestock losses. In order to better prevent and predict outbreaks of VBDs, many types of information and data on interactions of vectors with their environments over space and time need to be combined. However, efforts to do this have been hindered by data collected on vectors being isolated, difficult to access, and kept in disparate formats. The main goal of this project is to build a centralized open access data platform called VectorByte. It will contain standardized data on vector traits and population abundance. This will allow data to be more easily shared and used by the disease ecology community and by other interested communities. Further, freely available tools to analyze and model these data will be developed, combined with educational materials, including tutorials on using the databases and data analysis tools. Training early career scientists will be accomplished through workshops and mentoring of postdoctoral researchers, graduate students, and undergraduates within the project. Training workshops covering use of the databases and statistical methods appropriate for the data will target early career scientists from underrepresented groups and regions, as well as practitioners from the broader public health and vector control community. This combined audience will enable feedback from the applied realm about best user practice and will promote collaborative opportunities to bridge between tools developed within the academic community and real world decisions. The platform and training will in turn support research and mathematical modelling efforts that will lead to a better understanding of why outbreaks occur when and where they do and will allow for development and assessment of potential control strategies for these diseases. There is mounting empirical evidence that the traits of vectors vary across time, environmental conditions, and within and between populations. This variation has knock-on effects for the dynamics of vector populations, and therefore also for transmission of vector-borne infections and the efficacy of control strategies. Mathematical and statistical models can be used to better understand the links between traits, populations, and transmission. However, doing this well requires detailed data ranging from laboratory measurements of individual-level traits of vectors to observed population dynamics of the vectors, all of which are often difficult to obtain or use. Further, data for VBD systems are often archived in inconsistent formats and locations. The VectorByte project will develop a user-friendly informatics platform with a global scope for depositing, accessing, and visualizing data in order to fill these gaps for the VBD community. The VectorByte platform has the potential to transform VBD disease research by providing Findable, Accessible, Interoperable, and Reusable (FAIR) data, necessary to build, test, and validate models of VBDs not currently possible with available open data. This project will enable VBD researchers to increase the impact of their data through standardized formatting and centralized location, increasing the sustainability of data while simultaneously increasing the potential for reuse. These data standards will also facilitate comparison of VBD systems and the construction of open and testable models of VBD dynamics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

各种各样的陆生植物和动物病原体都会进化出透射循环，这些循环循环需要载体（通常是节肢动物），这些载体在宿主以宿主为食时会传播病原体。这些媒介传播疾病（VBD）对人类，动物和植物健康构成了严重威胁，并对全球经济福利产生负面影响。例如，大约三分之一的人口只有仅由蚊子传播的感染风险，并且向量传播了许多重要的植物和牲畜疾病。 VBD每年占人类传染病的17％和数十亿美元的农作物和牲畜损失。为了更好地防止和预测VBD的爆发，需要组合许多类型的信息和有关向量与其环境相互作用的数据。但是，为此做出的努力受到了对隔离媒介的数据，难以访问和保持不同格式的数据的阻碍。该项目的主要目标是构建一个名为vectorbyte的集中式开放访问数据平台。它将包含有关矢量特征和人口丰度的标准化数据。这将使疾病生态界和其他感兴趣的社区更容易共享和使用数据。此外，将开发出可用的分析和建模这些数据的工具，并结合教育材料，包括有关使用数据库和数据分析工具的教程。培训早期职业科学家将通过研讨会和指导博士后研究人员，研究生和本科生的指导来完成。涵盖数据库使用和适合数据的统计方法的培训研讨会将针对来自代表性不足的群体和地区的早期职业科学家，以及来自更广泛的公共卫生和媒介控制社区的从业人员。这种结合的受众将能够从应用领域的最佳用户实践中提供反馈，并将促进协作机会，以在学术界和现实世界中开发的工具之间桥接。该平台和培训将反过来支持研究和数学建模工作，这将使人们更好地了解为什么在何时何地发生爆发，并允许开发和评估这些疾病的潜在控制策略。有越来越多的经验证据表明，矢量的特征在时间，环境条件以及人群之间和人群之间有所不同。这种变化对向量种群的动力学具有敲门效应，因此也对向量传播感染的传播和控制策略的疗效。数学和统计模型可用于更好地了解特质，人群和传播之间的联系。但是，做得很好，需要详细的数据，从实验室测量向量的个体水平特征到观察到的向量的人群动态，所有这些都通常很难获得或使用。此外，VBD系统的数据通常以不一致的格式和位置存档。 Vectorbyte项目将开发一个用户友好的信息学平台，并具有用于存放，访问和可视化数据的全球范围，以便为VBD社区填补这些空白。 VectorByte平台有可能通过提供可发现的，可访问的，可互操作和可重复使用的（公平）数据来改变VBD疾病研究，这是通过可用的开放数据进行构建，测试和验证VBD模型所必需的。该项目将使VBD研究人员能够通过标准化的格式和集中位置来增加数据的影响，从而提高数据的可持续性，同时增加重复使用的潜力。这些数据标准还将促进VBD系统的比较，并构建VBD Dyn​​amics的开放和可测试模型。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子和更广泛的影响评估审查标准来评估值得支持。

项目成果

Parameterizing Lognormal state space models using moment matching

• DOI: 10.1007/s10651-023-00570-x
• 发表时间: 2023-07-15
• 期刊: ENVIRONMENTAL AND ECOLOGICAL STATISTICS
• 影响因子: 3.8
• 作者: Smith，John W.;Thomas，R. Quinn;Johnson，Leah R.
• 通讯作者: Johnson，Leah R.

Assessing Ecosystem State Space Models: Identifiability and Estimation

• DOI: 10.1007/s13253-023-00531-8
• 发表时间: 2023-03-09
• 期刊: JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS
• 影响因子: 1.4
• 作者: Smith Jr，J. W.;Johnson，L. R.;Thomas，R. Q.
• 通讯作者: Thomas，R. Q.