I am a theoretical biologist with an affinity for applied modeling, primarily within the fields of ecology and epidemiology. My research interests lie at the intersection of biological processes, statistics, mathematics, and computation. More often than not, my projects involve Bayesian analyses of structured data.

My research

Ecology and epidemiology are related fields of research. Both are ultimately concerned with interactions between organisms and their biotic and abiotic environment. They also share common challenges in terms of how complex processes need to be investigated from imperfect data. My research addresses these challenges by developing new analytical and predicative tools.

Livestock epidemiology

Outbreaks of Transboundary Animal Diseases (TADs) can be highly costly for the livestock sector due to disruption of production and export, and several TADs have painful symptoms, making them an animal welfare concern. They also pose a global threat to food security, and zoonosis events are a major concern for public health. The stakes are high for policy makers. Computer simulations that simulate the course of an outbreak can be used to as an important tool to aid policy decisions.

My research on livestock diseases has been funded by the United States Department of Agriculture (USDA) and Department of Homeland Security (DHS) involves developing simulation models that can be used to evaluate different control strategies. The US has the largest livestock industry in the world, making computational speed essential to swiftly evaluate multiple control options. Thus, my lab develops efficient computational algorithms for disease simulation models.

Applied epidemiological modeling is challenged by limited data. For instance, not all countries have federal databases with information about livestock movements. This information gap poses a problem for modeling of diseases where relocation of animals is a major risk factor. Focusing primarily on the US, my research aims to bridge this gap by extrapolating from available information and simulate nationwide movement networks such that they can be used in disease simulations.

Further reading:

Ensemble modeling

Multiple models have been proposed for the same livestock diseases. Sometimes these models provide conflicting recommendations for control strategies, making it challenging for policy makers to know which model to rely on. Ensemble modeling offers the ability to use multiple models collectively, a strategy that has improved predictions in other fields of research. The approach is still in its infancy for epidemiology, but my research is facilitating methods that promote the use of ensemble modeling.

Movement ecology

Movement ecology is concerned with the causes, mechanisms, patterns, and consequences organism movement. It is a growing field of research, driven by an ongoing technical revolution of tracking devices. Increasingly detailed movement data can be collected, necessitating new analytical tools. Focusing primarily on tropical Australia, my research has e.g. identified climatic drivers of reptile movement and highlighted the importance evolutionary processes during cane toad invasion.

Wildlife monitoring

Assessing bagging numbers is one of the most essential aspects of wildlife monitoring. In Sweden, reporting of number of animals killed is voluntary for most species, making it necessary to extrapolate from available reports to estimate the number of animals killed through hunting. In collaboration with the Swedish Association for Hunting and Wildlife Management, I’m developing statistical methods for this purpose.

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