Pathogen transmission networks in animal populations
I am broadly interested in how animal social and ranging behavior translates into contact networks, and consequently, transmission networks.
Some of my previous research in this area has focused on quantifying pathogen transmission patterns in wild and domestic ungulate populations in Kenyan savanna ecosystems. This work integrated social network theory and microbial genetics to quantify pathogen transmission networks in wild giraffe. I used the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission had occurred and used these data to construct transmission networks. By using microbial genetic data to quantify individuals that were part of the same chain of transmission independently from behavioral data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influenced the structure of the transmission network.
Wildlife-livestock Interfaces
Related to transmission networks in wildlife, one of my long-standing interests and key future research directions is understanding the dynamics of pathogen transmission between wild and domestic animals. Past work has focused on using E. coli to quantify multi-host transmission networks among cattle and wild African herbivores, with a specific objective of identifying species that are disproportionately important in pathogen transmission.
In addition, I am currently part of a collaborative project investigating transmission dynamics of foot-and-mouth disease (FMD) between African buffalo and cattle in Kenya, a project which combines field work, molecular epidemiology, and ultimately, network modeling.
Modeling disease spread in livestock
In addition to striving to understand Foot-and-mouth disease (FMD) transmission at the wildlife-livestock interface, I also am broadly interested in the epidemiology and ecology of FMD in endemic settings. Current and future projects will focus on the spatial epidemiology of FMD in Uganda, Vietnam, and India.
I also work extensively on modeling the spread of livestock diseases both within and between farms. Key current efforts in this area include modeling the spread of bovine tuberculosis in Uruguay and modeling the spread of diseases through swine movement networks in the US (Foot-and-mouth disease, Porcine epidemic diarrhea virus, & Porcine Reproductive and Respiratory Syndrome virus).
Research Overview.
Data Science and Analytics for Animal Health
In addition to the computational network modeling approaches described above, I am actively engaged in harnessing the “datafication” of the world to derive valuable and actionable insights from data. Major areas of interest include identifying high risk animal populations and forecasting pathogen risk across space and time. For example, my work has developed data cloud geometry approaches to identify multi-level hierarchical structure within data and Monte Carlo tests to evaluate the role of any given contact network in explaining the occurrence of disease. In addition, I have used big data tools such as ecological niche modeling to map environmental drivers of risk and machine learning techniques to predict links within networks and neighborhood effects on a farm’s risk of breaking with disease.
