Workshop Details

Climate change will expose new populations to diseases, by altering the range of temperature, precipitation, and other key meteorological variables, changing the areas in which the environment is suitable for these diseases to persist. However, this is a relatively slow process in comparison to the ability of diseases to jump between currently suitable environments via migration and travel. Cataloging, understanding, and ultimately predicting the movement of pathogens into naïve suitable environments in the current climate is necessary to anticipate and work to prevent the next crisis situation, and is the critical first step to understanding how disease risks will change in a changing climate.

Most studies of diseases ecology are focused on population dynamics to understand transmission, with metapopulation models dominating the scene. A gap of knowledge is how infectious diseases may respond to coarse-scale changes in the communities of hosts, the landscape, or the climate. A new frontier in disease ecology is the exploration of the macroecology of infectious diseases. In this very brief presentation, I will explain the conceptual bases of macroecology, and will show an example of macroecology applied to an infectious disease. Finally, I will explain the multiple opportunities to study infectious diseases using theory and methods from this field.

In this talk, I will provide an overview of my research interests, focused on using ecological niche modeling and spatial data to study species’ distributions. To illustrate my research interests, I will discuss studies (in progress or completed) aiming to detect plant species richness, plant traits, and estimate the potential distribution of species. In second part of my talk I will introduce the Spatial Analysis Lab, an interdisciplinary recharge center established last year in NIMBioS at University of Tennessee through a partnership between Ecology and Evolutionary Biology, Geography, NIMBioS, and the College of Arts and Sciences.

What makes the difference between an adequate model (useful in the short term for urgent decisions) and a robust, ecologically-supported one? In this talk I’ll present and unpack two problems. In the short term, consensus-building has become a major research and policy problem for mapping Zika virus—one that is still relevant, given tradeoffs between vector control and other ecological issues. In the long term, predicting how tick distributions will shift in response to climate change would be greatly improved by the ability to jointly model host and tick distributional shifts—especially given the role of host communities in tick-borne zoonoses like Lyme disease and Crimean-Congo haemorrhagic fever—but these technologies are severely underdeveloped. New models will be presented that address both of these case studies, and highlight where our field is headed in the face of accelerating threats.

Mapping emerging infectious diseases presents a challenge because these diseases tend to be understudied, with little known about the ecology of the disease or its vector. Data-driven predictive mapping allows us to draw inferences about diseases based on the statistical relationship between disease cases and environmental or socio-economic covariates, without explicitly modeling the underlying biology. In this short talk, I will present a case study of predictive mapping of yellow fever spillover risk in Brazil, and what it can tell us about the drivers of spillover. I will end by introducing recently georeferenced, crowd-sourced data for Zika in the Americas, and propose some opportunities to use this data to further our understanding of emerging vector-borne diseases.

Academic researchers, vector control districts, private companies, and the military generate and have been generating data on vector populations, including population estimates, insecticide resistance assays, and pathogen status testing. A case study on US mosquito data and solutions for unifying data, including the work of VectorBase, a repository for disease vector data, will be presented.

Traits of individuals, such as individual susceptibility to infection or behavior that determines contact rates, ultimately drive the observed dynamics of infections in populations. However, these traits are not often included explicitly into the traditional SIR-type models for infection transmission. In this talk I will focus on vector-borne infections generally, and on dengue specifically. I show a trait-based approach that allows data taken on individual vectors to be incorporated into a transmission model at the population level without explicitly modeling individuals. Further, we allow vector traits to depend on an environmental driver, specifically temperature. Most disease vectors are small arthropods, their life history process are very sensitive to temperature. We use a Bayesian approach to quantify how traits of the Aedes species mosquitoes depend on temperature. The thermal responses of these traits are incorporated into a dynamical model for the spread of dengue which is, summarized by a summary of the system based on the basic reproductive number R0. We also quantify the uncertainty of the traits and propagate this into uncertainty in R0. We validate this model using observed case data, and then explore predictions for global suitability for transmission both now and in the face of climate change.

Geostatistical methods are routinely used across many fields to reveal spatial structures in natural and anthropogenic phenomena. Nevertheless, many explicitly spatial analysis methods are still novel in public health practice, oftentimes absent from routine agency surveillance activities. Here, I present a brief overview of projects that leveraged existing georeferenced entomological surveillance and human case data from health agencies, applying diverse methods including ecological niche modeling, spatial network analysis, local indicators of spatial association (LISA), and space-time scan statistics. These projects exemplify the utility, and potential, of incorporating nontraditional methodologies into existing surveillance frameworks.

This work presents the first mathematical models for the life cycle of Ornithodoros moubata, an argasid tick. Ornithodoros moubata is an important tick species throughout Africa and Europe and is a known vector for African swine fever (ASF), a catastrophically fatal disease for domesticated pigs in Africa and Europe. We present a continuous-time differential equation model that simplifies the tick life cycle to two stages, and a discrete-time difference equation model that uses four life cycle stages. Both models utilize two host types: small hosts and large hosts, which are representative of natural populations. The models find that either host type alone could support the tick population and that the final tick density is a function of host density. While both models predict similar tick equilibrium values, we observe significant differences in the time to equilibrium. The results demonstrate the likely establishment of these ticks if introduced into a new area even if there is only one type of host. These models provide the basis for developing future models that include disease states to explore infection dynamics and possible management of ASF.

Southeastern Virginia is a mixing bowl for ticks and tick-borne pathogens with recent changes sweeping in from both the north and the south. We have been monitoring the ticks and pathogens with active surveillance since 2009 in the Hampton Roads area of Virginia. We are using the data from our surveillance to parameterize mathematical models to better understand the changing conditions in the region and the relationship between these changes and climate change.

Diagnosed cases of mosquito- and tick-borne diseases are increasing at an alarming rate. Without surveillance efforts, vector ranges slowly expand and colonizing and invasive species often go undetected. My collaborative research program addresses Vector Biology and One Health by integrating entomology with genetic analyses, pest management, ecology, modeling, and epidemiology for vector and pathogen suppression through integration and evaluation of control elements, such as habitat management, education, and appropriate control technology. I will use this time to introduce some of our research projects, which involves the study of ticks, mosquitoes, and flies.