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）是一类影响人类、野生动物、牲畜和植物的重要感染。在一些区域，疟疾和登革热等生物杀灭疾病在以前的消灭运动减弱的地方死灰复燃。在其他地方，媒介和病原体的新传入和传播，例如亚洲柑橘木虱及其黄龙病，是由于人类促进的传入（长途旅行事件，航运集装箱等）而发生的。vbd的传播是复杂的。我们观察到的传播模式是由病媒、病原体、宿主及其环境之间的相互作用决定的。大多数病媒是小型节肢动物；它们对环境条件很敏感，比如温度或降雨。至关重要的是，要了解这些因素和条件如何影响病媒的动态，以便更好地为监测和减轻野生生物灭绝的战略提供信息。这个职业项目将进一步了解环境介导的病媒特征（如寿命和繁殖力）如何与环境因素相互作用，从而影响VBDs的传播。将开发改进的定量方法，以预测何时何地会发生传播，并估计这些预测中的不确定性。本CAREER项目解决了三个主要问题：(1)我们如何量化和模拟环境驱动因素对病媒多种相关特征的影响，从而对VBD传播动力学产生影响？(2)我们如何将机制或基于模型的传播措施与发病率/传播的统计模型联系起来，以改进对VBD传播或出现的时间和地点的预测？(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？我们如何以一种对决策有用的方式传达这种不确定性？该项目将以一个基于特征的框架来解决这些问题，最初将重点放在登革热和黄龙冰这两个截然不同但数据丰富的研究系统上。更具体地说，将首先开发包括环境介导的矢量特征（包括特征相关性和异质性）细节的机械数学模型。这些模型将被参数化，并使用贝叶斯方法使用来自开放数据存储库的数据进行验证。将开发方法和工具，以验证这些模型，适当量化不确定性的来源和类型，以及在不确定性下测试干预措施和制定政策决定。通过对弗吉尼亚卫生部现有研究人员进行统计培训和合作的综合办法，该项目将提高未来研究人员、公共卫生官员和决策者的数量和统计能力。具体而言，工作将包括：a)为VDH员工开发工具和培训材料，以使用尖端的建模技术；b)通过与世界卫生组织的互动，为本科生、研究生和博士后研究人员提供公共卫生合作方面的培训和经验；c)通过恢复弗吉尼亚理工大学的生物统计学课程，对本科生物学家进行统计学培训；d)本科定量生物学研究经验，重点是vbd；e)通过统计学教育学研讨会课程和共同指导本科生研究人员的机会，培养研究生的教学和指导能力。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.