David Rasmussen

While microbial pathogens such as RNA viruses exhibit a remarkable capacity to adapt to novel host environments, their potential to emerge on new hosts and expand their host range is not limitless. Understanding what constrains the potential of pathogens to adapt to new hosts could therefore offer major insights on how to better control and manage microbial pathogens. However, understanding what limits host range requires a detailed understanding of pathogen fitness not just in different hosts, but also across different scales at the within and between host levels. To address these challenges, I propose to develop new computational methods based on birth-death models that can be used to quantify pathogen fitness across hosts and scales using pathogen genomic data. These new methods will be applied to study the evolutionary dynamics of host range in tomato spotted wilt virus (TSWV), an important plant pathogen worldwide. By characterizing the host range of natural TSWV isolates collected in the field and experimentally evolving the virus������������������s host range in the lab, we will determine the factors that limit the evolutionary potential of the virus to adapt to new hosts and expand its host range.

Healthcare-associated infections (HAI) are a significant source of preventable morbidity and mortality. Transmission models for HAI are a cornerstone method to both understand pathogen spread and evaluate control interventions. Models have been particularly helpful in addressing transmission-blocking interventions, for elucidating the connectivity among facilities, and their implications for controlling HAI. Mechanisms underlying antimicrobial resistance, such as co-selection, have received less attention in transmission models. In addition, key metrics������������������such as population-level fitness of resistant bacteria and the effect of resistant traits on fitness������������������are often unknown. This limits our understanding of the complex relationship between antimicrobial drug use and resistance, as well as the effectiveness of interventions aimed at changing drug selection pressure. The objective of this proposal is to develop models that more explicitly address resistance traits and modeling tools that support the identification of transmission sources and pathways for HAI. We will use the models to further identify HAI sources and evaluate and optimize interventions. In particular, we will address the following thematic areas: antimicrobial resistance (A), surveillance (A), genomics (B), and simulation of epidemiological studies (B). We have assembled an interdisciplinary group of researchers with expertise in infectious disease modeling, HAI hospital epidemiology and clinics, applied mathematics, and genomics located at North Carolina State University, Washington University (WU) and University of Tennessee. We plan to build on our previous and current collaborations among this team to: develop modeling approaches for addressing HAI transmission; extend phylodynamics methods; and model antimicrobial resistance dynamics. The CDC-Epi Center at WU and Barnes-Jewish Hospital in St. Louis, Missouri, will be the main source of data. Additionally, we will use nation-level publicly available data sources. We will carry out the following aims: 1) Develop improved approaches for inferring routes of acquisition of HAI and optimizing HAI surveillance and control: We will develop ward- and hospital- level network models that take into account the main routes of HAI acquisition and patient connectivity. We will apply optimization methods to identify environmental sampling protocols and cost-effective control strategies. 2) Phylodynamics to estimate fitness of antimicrobial resistance pathogens: We will apply and refine multi-type birth-death models to explore the fitness effects of a large number of antimicrobial-resistant traits on pathogen phylogenies, and speed the methods to quantify fitness for large numbers of strains, and 3) Multi-scale models for multidrug-resistant organisms: extended-spectrum beta-lactamase (ESBL)- producing Enterobacteriaceae as case study: We will develop both agent- and equation-based models that account for multi-scale dynamics of resistance transmission. This will greatly expand the models������������������ applications for evaluating interventions such as antimicrobial stewardship and rapid testing. Our models and tools will be made available to the broader community.

Develop new statistical methods for reconstructing contract/transmission networks from diverse data streams and assist in testing the performance of these methods using empirical pathogen genomic datasets.

Emerging plant disease and pest outbreaks reduce food security, national security, human health, and the environment, with serious economic implications for North Carolina growers. These outbreaks may accelerate in coming decades due to shifts in the geographic distributions of pests, pathogens and vectors in response to climate change and commerce. Data-driven agbioscience tools can help growers solve pest and disease problems in the field more quickly but there is an urgent need to harness game-changing technologies. Computing devices are now embedded in our personal lives with sensors, wireless technology, and connectivity in the ����������������Internet of Things��������������� (IoT) but these technologies have yet to be scaled to agriculture. Our interdisciplinary team will build transformative sensor technology to identify plant pathogens, link local pathogen data and weather data, bioinformatics tools (pathogen genotypes), and use data driven analytics to map outbreaks, estimate pest and pathogen risk and economic damage, in order to coordinate response to emerging diseases, and contain threats. Sensor-supported early and accurate detection of pathogens before an outbreak becomes wide-spread in growing crops will significantly reduce pesticide use and increase crop yields.

We propose to develop new methods for tracking the spread of plant pathogens through agricultural landscapes using population genetic data. Because plant pathogens spread across complex landscapes, our approach will build on network models from spatial epidemiology that provide the flexibility needed to track epidemic dynamics across multiple scales and locations. Network models will be combined with phylogenetic approaches for estimating spatial spread based on the genetic relatedness of pathogens sampled at different geographic locations. These methods will then be implemented in high-performance, user-friendly software for analysis and web-based visualization. We aplan to apply our approach to study the spatial epidemic dynamics to three crop pathogens of major economic importance: Barley yellow dwarf virus, the aflatoxin-producing mold Aspergillus flavus and the downy mildew Pseudoperonospora cubensis. By synthesizing advances in spatial epidemiology and population genetics, our approach will provide next-generation software tools that will help reveal the dominant pathways by which these pathogens spread and identify major geographic sources that future control strategies can target.

The project is to support the state of North Carolina in surveillance of SARS-CoV-2 variants in the population. We will sequence SARS-CoV-2 from clinical specimens collected as part of the NCSU surveillance lab (surveillance lab director Megan Jacob is a co-PI) and partner WakeMed hospitals. We estimate to sequence 3700 samples over the course of the project.