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

病媒传播疾病(VBD)是影响人类、野生动物、牲畜和植物的一种重要传染病。在一些地区，疟疾和登革热等VBD在之前的消灭运动减弱的情况下死灰复燃。在其他地方，媒介和病原体的新引入和传播，例如亚洲柑橘木虱及其随之而来的黄龙病(柑橘绿化)，是由于人为引入(长途旅行活动、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；它们对环境条件敏感，如温度或降雨。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缓解VBDS的战略提供信息，这一点至关重要。这一职业项目将进一步加深对环境介导性媒介特征(例如寿命和繁殖力)如何与环境因素相互作用影响VBD传播的总体理解。将开发改进的定量方法来预测传播将在何时何地发生，并估计这些预测中的不确定性。这个职业项目涉及三个主要问题：(1)我们如何对环境驱动因素对多种相关特征的影响进行量化和建模，从而对VBD传播动力学产生影响？(2)我们如何将传播的机械性或基于模型的测量与发病/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，以及我们如何以一种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，最初将重点放在两个截然不同但数据丰富的研究系统--登革热和黄龙冰。更具体地说，将首先开发机械性数学模型，其中包括环境中介媒介特性的细节，包括特性相关性和异质性。这些模型将被参数化，并使用来自开放数据仓库的数据使用贝叶斯方法进行验证。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，并在不确定情况下测试干预措施和作出政策决定。通过统计培训的综合方法以及与弗吉尼亚州卫生部现有研究人员的合作，该项目将提高未来研究人员、公共卫生官员和政策制定者的量化和统计能力。具体来说，这项工作将包括：a)开发工具和培训材料，供vdh员工使用尖端建模技术；b)通过与vdh的互动，为本科生、研究生和博士后研究人员提供公共卫生协作方面的培训和经验；c)通过弗吉尼亚理工学院振兴的生物统计学课程，培训本科生生物学家；d)本科生在定量生物学方面的研究经验，重点是vbds；E)通过统计教育学研讨会课程和共同指导本科生研究人员的机会，对研究生进行教学和指导方面的培训。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.