Faculty Profile

Biography

Dr. Lofgren is an infectious disease epidemiologist whose research focuses on the use of mathematical and computational models of disease transmission, particularly the transmission of antimicrobial resistant infections within and outside healthcare settings, as well as emerging infectious diseases. His work often focuses on producing policy-relevant results, working hand-in-hand with clinicians and policy makers to produce reproducible, quantitative guidance for designing and evaluating public health interventions.

Personal Statement

My path is what happens when you have a computer nerd Biology major who comes to the realization that they’re not interested in being a doctor. Introduced to epidemiology while studying abroad in Ireland, I became fascinated with the idea of understanding how diseases worked not on the scale of an individual microbe or person, but on the scale of an entire population. That allowed me to work on a wide variety of different questions, from how atmospheric conditions impact microbial survival to how human societies choose to organize themselves – and how that potentially makes them sick. What’s followed from that has been an engaging career – being able to address pressing and timely public health questions using mathematical and computational tools.

When not working, I spend my free spoiling my two dogs, Felix and Gunner.

Education and Training

• BA (Biology) – Tufts University, Boston, MA
• MSPH (Epidemiology) – University of North Carolina, Chapel Hill, NC
• PhD (Epidemiology) – University of North Carolina, Chapel Hill, NC
• Postdoc – Network Dynamics and Simulation Science Lab, Virginia Tech, Blacksburg, VA

Curriculum Vitae

General Research / Expertise

My lab uses mathematics and computer simulations to study the spread of infectious diseases, and develop new ways to control them. We work on a broad range of pathogens in many different settings, including:

• Controlling healthcare-associated infections as part of the CDC’s Modeling Infectious Diseases in Healthcare Network (https://www.cdc.gov/hai/research/MIND-Healthcare.html)
• Infections between humans and companion animals (dogs and cats.
• Combatting antibiotic resistance in both the developed and developing world
• Rabies elimination in Kenya

Research Contributions

The use of mathematical and computational models in the study of infectious diseases is especially useful in situations where conventional studies are hard to conduct. These include public health emergencies like an ongoing epidemic, national or state-level policy decisions, and complex environments like healthcare facilities.

Dr. Lofgren’s work has been focused on providing pragmatic modeling results to aid policy makers, clinicians, and others in making informed decisions about public health. It has been used in a number of different settings, from evaluating infection control programs in hospitals to providing forecasts for the West African Ebola epidemic.

Research Details

The Lofgren Lab works at the interface of mathematical/computational epidemiology and conventional observational approaches. We focus on the use of stochastic models to study transmission beyond direct person-to-person contact, including environmental contamination with fomites, zoonotic transmission, etc. Of special interest are healthcare-associated infections, both within and between healthcare facilities, as well as antimicrobial resistant pathogens and emerging infectious diseases.

These models rely on robust, reliable parameter estimates. The Lofgren Lab uses the best available statistical techniques to estimate these parameters. 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.

The goal of this research is to produce “policy relevant” results – that is models that provide meaningful, actionable advice to clinicians, policy makers, and other public health professionals. This includes developing methods to cohere observational and computational results together to provide a more complete picture of disease dynamics as a whole, as well as working closely with collaborators in the clinic and in the field.