Current Research in Epidemiology and Biosecurity
Our lab is interested in the mathematical modeling of infectious disease dynamics, especially how individual behaviors and contact patterns cause different outcomes at the level of the population. Our work is both theoretical, developing new mathematical methods, and applied, employing a diversity of modeling techniques to explore best practical methods for public health interventions. We focus not only on health outcomes, but also issues of economic impacts and/or societal stability. Lastly, we are interested in novel methods for biosurveillance and outbreak detection and how best to employ surveillance systems with epidemiological modeling to achieve the most efficient preparedness and intervention strategies.
Our interests in this area are always growing, but we keep revisiting a few central themes.
- Social Behavior and Population Robustness to Disease Threats: Are there ways that individuals could behave, or individuals could organize under normal circumstances that wouldn't interfere with ordinary life, but would offer protection against epidemic spread once an infectious disease outbreak is under way? How do individuals assess risks of infection? Do they alter their behaviors in response to disease risks? And, if so, how do those alterations change the course of an epidemic on the population level? How can these processes be incorporated effectively into mathematical models in epidemiology?
- Biosurveillance: Current methods of outbreak detection rely on knowledge of prior disease incidence to define "normal" vs. "outbreak" conditions. Are these definitions necessary in order to determine when an epidemic is beginning? Can we discover new algorithms to handle multiple data sources, coming in at different rates, with different sensitivities, while still maintaining a method for detection which will alert us to every outbreak, and only when an outbreak is starting?
- Economics and Epidemiology: How much do public health strategies to combat infectious diseases cost? How best can we use limited resources to maintain public health? Are individual health and economic incentives and trade-offs aligned with the public good? Do public health burdens from infectious disease cause accidental differences in economic burdens to different facets of society in unexpected ways? How can we try to exploit this perspective to design public health strategies? While a lot of this involves economic costs, we also look at emotional costs and benefits (i.e. utilities).
Lofgren, E., M. Senese, J. Rogers and N.H. Fefferman. Pandemic Preparedness Strategies for School Systems: Is Closure Really the Only Way? Annales Zoologici Fennici, 45: 449-458.
Fefferman, N.H. and K.L. Ng. 2007. Can Disease Models on Static Graphs Approximate Epidemics in Shifting Social Networks? Physical Review E. 76:031919. (This article was selected for reprinting by the Virtual Journal of Biological Physics Research in October 2007)
Lofgren, E. and N.H. Fefferman. 2007. The Untapped Potential of Virtual Game Worlds to Shed Light on Real World Epidemics. The Lancet Infectious Diseases. 7:625-629.
Lofgren, E., N.H. Fefferman, Y.N. Naumov, J. Gorski and E.N. Naumova. 2007. Influenza Seasonality: Underlying Causes and Modeling Theories. Journal of Virology, 81(11):5429-5436.
Lofgren, E., N.H. Fefferman, M. Doshi and E.N. Naumova. 2007. Assessing Seasonal Variation in Multisource Surveillance Data: Annual Harmonic Regression. Lecture Notes in Computer Science. BioSurveillance 2007. eds D. Zeng et al. 4506:114-123.
Fefferman, N.H., and E.N. Naumova. 2006. Combinatorial Decomposition of an Outbreak Signature. Mathematical Biosciences, 202(2):269-287.
Fefferman, N.H., E.A. O′Neil, and E.N. Naumova. 2005. Confidentiality vs Confidence: The aggravation of aggregation as a remedy in public health. Journal of Public Health Policy, 26(4):430-449.