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

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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.