My research primarily involves the mathematical and computational modeling of infectious disease dynamics, a powerful tool for obtaining insight in circumstances where conventional observational methods are impossible, unfeasible or unethical. Of particular interest to me is the dynamics of healthcare-associated infections, where a number of diseases spread in an artificially constructed environment under intense selective pressure. Much of my work in this area focuses on the prevention and treatment of Clostridium difficile infections, either through patient-centric treatment interventions or interventions within the hospital environment, such as improved surface disinfection. This interest also extends to pathogens that spread through an individual’s social contact network due to hand contamination, large droplet aerosols, etc. such as norovirus and influenza, as well as the exchange of diseases between the healthcare system and the surrounding community.
I have also begun to work on problems surrounding the compact summarization of model results, either numerically or visually, in a way that conveys the wealth of data that emerges from mathematical modeling studies without introducing ambiguity.
Most Recent Publications:
Rivers, C.M., E.T. Lofgren, M.V. Marathe, S.G. Eubank, B.L. Lewis. 2014. Modeling the Impact of Interventions on an Epidemic of Ebola in Sierra Leone and Liberia. PLoS Currents Outbreaks
Lofgren, E.T., R.W. Moehring, D.J. Weber, D.J. Anderson, N.H. Fefferman. 2014. A Mathematical Model to Evaluate the Routine Use of Fecal Transplantation to Prevent Incident and Recurrent Clostridium difficile Infection. Infection Control and Hospital Epidemiology, 35(1):18-27
Lofgren, E.T. 2012. Visualizing Results from Transmission Models: A Case Against ‘Confidence Intervals’. Epidemiology, 23(5): 738-741
Mathematical models 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.
Observational epidemiology also stands to benefit from mathematical modeling, where models provide potential avenues for research, identify areas where there is little or no present information even in supposedly well-studied diseases, or testing potential interventions in a virtual environment. My work seeks to develop methods to cohere observational and theoretical results together in a way that promotes collaboration and transfer of knowledge between observational epidemiologists and mathematical modelers, such as the analysis of modeled populations as “virtual cohorts”.
Most Recent Publications:
Lofgren, E.T., S.R. Cole, D.J. Weber, D.J. Anderson, R.W. Moehring. 2014. Estimating all-cause mortality and length of stay in incident, healthcare facility-associated Clostridium difficile cases using parametric mixture models. Epidemiology, 25(4): 570-575
Moehring, R.W., E.T. Lofgren, D.J. Anderson. 2013. Impact of Change to Molecular Testing for Clostridium difficile Infection on Healthcare Facility-Associated Incidence Rates. Infection Control and Hospital Epidemiology, 34(10): 1055-1061