Dr. Lofgren’s research focuses on the epidemiology of infectious diseases, particularly in the areas of antimicrobial resistance, healthcare-associated infections, and emerging and zoonotic pathogens. Specifically, his research touches on several broad themes:
- Mathematical and Computational Modeling: Mathematical and computational models of infectious disease dynamics are a powerful tool for obtaining insight in circumstances where conventional observational methods are impossible, unfeasible or unethical. Of particular interest are transmission dynamics beyond person-to-person contact, including environmental contamination, zoonotic diseases, etc.
- Observational Epidemiology: The models outlined above often rely on observational study data to inform parameter choices or test predictions. The reliability of these models thus hinges on the availability of quality, unbiased study results amenable for inclusion in mathematical models. My research examines the use of modern epidemiological methods to provide these results. This includes, but is not limited to, the use of parametric survival models to analyze cohort studies, meta-analysis, biosurveillance, and the evaluation of the population-level accuracy of diagnostic tests to identify the burden of disease in populations. This research also extends to methods to cohere observational and computational results together to provide a more complete picture of disease dynamics as a whole.
- Quantifying Uncertainty: Mathematical and computational models are able to make predictions in the presence of flawed and incomplete data, or considerable uncertainty about the possible trajectory of an epidemic, driven by changing conditions on the ground, or random chance. Dr. Lofgren uses a number of stochastic simulation and data visualization techniques to attempt to quantify this uncertainty, and communicate it to decision-makers.