CAREER: Quantifying heterogeneity and uncertainty in the transmission of vector borne diseases with a Bayesian trait-based framework

职业：利用基于贝叶斯特征的框架量化媒介传播疾病传播的异质性和不确定性

• 批准号: 1750113
• 负责人: Leah Johnson
• 金额: $ 70万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750113&HistoricalAwards=false
• 关键词: CAREER; Quantifying; heterogeneity; uncertainty; transmission

Vector-borne diseases (VBDs) are an important class of infections that impact humans, wildlife, livestock, and plants. In some regions, VBDs, such as malaria and dengue, have resurged where previous elimination campaigns have waned. In other places, the novel introduction and spread of vectors and pathogens, for example the Asian citrus psyllid and with it huanglongbing (citrus greening), is occurring due to human facilitated introductions (long distance travel events, shipping containers, etc.). Transmission of VBDs is complex. The patterns of transmission that we observe are determined by interactions between vectors, pathogens, hosts, and their environment. Most disease vectors are small arthropods; they are sensitive to environmental conditions, such as temperature or rainfall. It is vitally important to understand how these factors and conditions affect the dynamics of the vectors in order to better inform strategies for monitoring and mitigation of VBDs. This CAREER project will further a general understanding of how environmentally-mediated traits of vectors (e.g. longevity and fecundity) interact with environmental factors to impact the transmission of VBDs. Improved quantitative methods will be developed for predicting when and where transmission will occur and for estimation of the uncertainty in these predictions. This CAREER project addresses three main questions: (1) How do we quantify and model the impacts of environmental drivers on multiple correlated traits of vectors and thus on VBD transmission dynamics? (2) How can we link mechanistic or model-based measures of transmission with statistical models of incidence/transmission to improve predictions of when and where VBD transmission or emergence may occur? (3) How can we integrate multiple sources of uncertainty into our models/predictions and how can we communicate this uncertainty in a way that is useful for decision making? The PI will tackle these questions with a trait-based framework, initially focusing on two very different but data rich study systems, dengue and huanglongbing. More specifically, mechanistic mathematical models that include details on environmentally mediated vector traits, including trait correlation and heterogeneity, will first be developed. These models will be parameterized and validated with data from open data repositories using a Bayesian approach. Methods and tools will be developed for validating these models, for properly quantifying sources and types of uncertainty, and for testing interventions and making policy decisions under uncertainty. Through an integrated approach to statistical training and collaboration with current researchers in the Virginia Department of Health (VDH), the project will improve the quantitative and statistical capabilities of future researchers and public health officials and policy makers. In particular, the work will include: a) Development of tools and training materials for VDH employees to use cutting edge modeling techniques; b) Training and experience for undergraduates, graduate students, and postdoctoral researchers in public health collaboration through interactions with the VDH; c) Training of undergraduate biologists in statistics through a revitalized Biological Statistics course at Virginia Tech; d) Undergraduate research experience in quantitative biology with a focus on VBDs; e) Training of graduate students in teaching and mentoring through a seminar course on statistics pedagogy and through opportunities to co-mentor undergraduate researchers.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.

媒介传播疾病（VBD）是影响人类，野生动植物，牲畜和植物的重要类型。在某些地区，疟疾和登革热等VBD在以前的淘汰运动减弱的地方恢复了。在其他地方，由于人类促进的引入（长距离旅行事件，运输容器等），发生了新颖的载体和病原体的新介绍和传播，例如亚洲柑橘花园及其huanglongbing（柑橘绿色）。 VBD的传输很复杂。我们观察到的传播模式取决于向量，病原体，宿主及其环境之间的相互作用。大多数疾病媒介是小节肢动物。它们对温度或降雨等环境条件敏感。了解这些因素和条件如何影响向量的动态，以便更好地为监测VBD的策略提供信息，这一点至关重要。这个职业项目将进一步了解对向量的环境介导的特征（例如寿命和繁殖力）如何与环境因素相互作用，以影响VBD的传播。将开发改进的定量方法，以预测在何时何地发生传输以及这些预测中的不确定性。 该职业项目解决了三个主要问题：（1）我们如何量化和建模环境驱动因素对向量的多个相关性状，从而对VBD传输动态的影响？ （2）我们如何将传输的机械或模型度量与入射率/传播的统计模型联系起来，以改善对VBD传播或出现何时何地的预测？ （3）我们如何将多种不确定性来源整合到我们的模型/预测中，以及如何以对决策有用的方式传达这种不确定性？ PI将通过基于特征的框架来解决这些问题，最初着重于两个非常不同但数据丰富的研究系统，即登革热和黄隆。更具体地说，将首先开发有关环境介导的向量性状（包括特征相关性和异质性）的机械数学模型。 这些模型将使用贝叶斯方法的开放数据存储库中的数据进行参数化和验证。将开发方法和工具来验证这些模型，适当量化不确定性的来源和类型，并测试干预措施并在不确定性下做出政策决策。通过与弗吉尼亚卫生部（VDH）现任研究人员进行统计培训和合作的综合方法，该项目将提高未来研究人员以及公共卫生官员以及决策者的定量和统计能力。特别是，该工作将包括：a）开发工具和培训材料，以供VDH员工使用前沿建模技术； b）通过与VDH的互动，为大学生，研究生和博士后研究人员培训和经验； c）通过弗吉尼亚理工大学的复兴生物统计课程培训统计学的本科生物学家； d）定量生物学的本科研究经验，重点是VBD； e）通过有关统计教学法的研讨会课程以及通过授权本科研究人员的机会培训研究生在教学和指导方面。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子优点和更广泛的影响来通过评估来获得支持。

项目成果

Batch-sequential design and heteroskedastic surrogate modeling for delta smelt conservation

三角洲冶炼保护的批量顺序设计和异方差替代模型

• DOI: 10.1214/21-aoas1521
• 发表时间: 2022
• 期刊: The Annals of Applied Statistics
• 作者: Zhang, Boya;Gramacy, Robert B.;Johnson, Leah R.;Rose, Kenneth A.;Smith, Eric
• 通讯作者: Smith, Eric

Modeling Temperature Effects on Population Density of the Dengue Mosquito Aedes aegypti

• DOI: 10.3390/insects10110393
• 发表时间: 2019-11-01
• 期刊: INSECTS
• 影响因子: 3
• 作者: El Moustaid, Fadoua;Johnson, Leah R.
• 通讯作者: Johnson, Leah R.

Analyzing Stochastic Computer Models: A Review with Opportunities

• DOI: 10.1214/21-sts822
• 发表时间: 2020-02
• 期刊: Statistical Science
• 影响因子: 5.7
• 作者: Evan Baker;P. Barbillon;A. Fadikar;R. Gramacy;Radu Herbei;D. Higdon;Jiangeng Huang;L. Johnson

Predicting the fundamental thermal niche of crop pests and diseases in a changing world: A case study on citrus greening

• DOI: 10.1111/1365-2664.13455
• 发表时间: 2019-07-01
• 期刊: JOURNAL OF APPLIED ECOLOGY
• 影响因子: 5.7
• 作者: Taylor, Rachel A.;Ryan, Sadie J.;Johnson, Leah R.
• 通讯作者: Johnson, Leah R.

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.