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.

  1. 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?
  2. 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?
  3. 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).

Related Publications

How disease constrains the evolution of social systems. Udiani, O. and N.H. Fefferman. 2020. Proceedings of the Royal Society B. 287(1932):20201284.

The Impact of Host Metapopulation Structure on Short-term Evolutionary Rescue in the Face of a Novel Pathogenic Threat. Jiao, J., M. Gilchrist, and N.H. Fefferman. 2020. Global Ecology and Conservation. e01174.

Impact of Strain Competition on Bacterial Resistance in Immunocompromised Populations DeNegre, A., Myers, K., and N.H. Fefferman. 2020. Antibiotics. 9(3):114.

Coordination among neighbors improves the efficacy of Zika control despite economic costs Lemanski, N.J., S.R. Schwab, and D.M. Fonseca, and N.H. Fefferman. 2020. PLoS neglected tropical diseases 14(6):e0007870.

How Resource Limitations and Household Economics May Compromise Efforts to Safeguard Children During Outbreaks. Myers, K., A. Redere, and N.H. Fefferman. 2020. BMC Public Health. 20(1):1-14.

A Generic Arboviral Model Framework for Exploring Trade-offs Between Vector Control and Environmental Concern. Suarez, G., O. Udiani, B. Allan, C. Price, S. Ryan, E. Lofgren, A. Coman, C. Stone, L. Gallos, and N.H. Fefferman. 2020. Journal of Theoretical Biology. 490 (2020) 110161.

How Emergent Social Patterns in Allogrooming Combat Parasitic Infections Wilson, S.N., S.S. Sindi, H.Z. Brooks, M.E. Hohn, C.R. Price, A.E. Radunskaya, N.D. Williams, and N.H. Fefferman. 2020. Frontiers in Ecology and Evolution. 8:54.

Impact of Chemoprophylaxis Policy for AIDS-immunocompromised Patients on Emergence of Bacterial Resistance DeNegre, A., Myers, K., and N.H. Fefferman. 2020. PLoS One. 15(1): e0225861.

Contrasting the Value of Targeted vs. Area-Wide Mosquito Control Scenarios to Limit Arbovirus Transmission for Different Tropical Urban Population Centers. Stone, C., S. Schwab, D. Fonseca, and N.H. Fefferman. 2019. PLoS Neglected Tropical Diseases. 13.7: e0007479.

Emergence of Antibiotic Resistance in Immunocompromised Host Populations. DeNegre, A., K. Myers, M. Ndeffo, and N.H. Fefferman. 2019. PLoS One 14 (2), e0212969.

(Meta)population Dynamics Determine Effective Spatial Distributions of Mosquito-Borne Disease Control. Schwab, S., C. Stone, D. Fonseca, and N.H. Fefferman. 2019. Ecological Applications 29(3): e01856.

How Disease Risks Can Impact the Evolution of Social Behaviors and Emergent Population Organization. Williams, N.D., H.Z. Brooks, M.E. Hohn, C. R. Price, A.E. Radunskaya, S.S. Sindi, S.N. Wilson, and N. H. Fefferman. 2018. in Understanding Complex Biological Systems with Mathematics eds. A. Radunskaya, R. Segal, B. Shtylla. Association for Women in Mathematics Series, vol 14. pp 31-46. Springer.

The importance of being urgent: the impact of surveillance target and scale on mosquito-borne disease control. Schwab, S., C. Stone, D. Fonseca, and N.H. Fefferman. 2018. Epidemics. 23:55-63.

Human movement, cooperation, and the effectiveness of coordinated vector control strategies. Stone, C.M., S.R. Schwab, D.M. Fonseca, N.H. Fefferman. 2017. Journal of the Royal Society Interface. 14(133):20170336.

Plagues and people: Mass community participation in a virtual epidemic within a tween online world. Fields, D. A., Kafai, Y. B., Giang, M. T., Fefferman, N., & Wong, J. 2017. Proceedings of the 12th International Conference on the Foundations of Digital Games. DOI: 10.1145/3102071.3102108.

Relative Risk of Infection with Ehrlichiosis Agents and Lyme Disease in an Area Where Both Vectors are Sympatric. Egizi, A., N.H. Fefferman, and R. Jordan. 2017. Emerging Infectious Diseases. 23(6):939-945.

The Dragon Swooping Cough: Mass community participation in a virtual epidemic within a tween online world. Fields, D. A., Kafai, Y. B., Giang, M. T., Fefferman, N., & Wong, J. 2017. In B. Smith, M. Borge, E. Mercier & K. Y. Lim (Eds.) Proceedings of the 12th International Conference on Computer Supported Collaborative Learning, Volume 2 (pp. 865-866). Philadelphia, PA: International Society of the Learning Sciences.

Patients as Patches: Urban Ecology and Epidemiology in Healthcare Environments. Lofgren, E., A. Egizi, and N.H. Fefferman. 2016. Infection Control and Hospital Epidemiology. 37(12):1507-1512.

The Effect of Disease-Induced Mortality on Structural Network Properties. Gallos, L., and N.H. Fefferman. 2015. PLoS One. DOI: 10.1371/journal.pone.0136704.

Dangers of vaccine refusal near the herd immunity threshold: a modelling study. Fefferman, N.H. and E.N. Naumova. 2015. Lancet Infectious Diseases. S1473-3099(15)70130-1.

A Mathematical Model to Evaluate the Routine Use of Fecal Microbiota Transplantation to Prevent Incident and Recurrent Clostridium difficile Infection. Lofgren, E.T., R.W. Moehring, D.J. Anderson, D.J. Weber, and N.H. Fefferman. 2014. Infection Control and Hospital Epidemiology. 35(1):18-27.

Deviations in influenza seasonality: odd coincidence or obscure consequence? Moorthy, M., D. Castronovo, A. Abraham, S. Bhattacharyya, S. Gradus, J. Gorski, Y.N. Naumov, N.H. Fefferman, and E.N. Naumova. 2012. Clinical Microbiology and Infection. 18(10):955-962.

Social organization patterns can lower disease risk without associated disease avoidance or immunity. Hock, K. and N.H. Fefferman. 2012. Ecological Complexity. 12:34–42.

Disproportional effects in populations of concern for pandemic influenza: insights from seasonal epidemics in Wisconsin, 1967–2004. Lofgren, E. T., Wenger, J. B., Fefferman, N. H., Bina, D., Gradus, S., Bhattacharyya, S., Naumov, Y. N., Gorski, J. and Naumova, E. N. 2010. Influenza and Other Respiratory Viruses, 4: 205–212.

Innovation in Observation: A Vision for Early Outbreak Detection Fefferman, N.H. and E.N. Naumova. 2010. Emerging Health Threats.Vol. 3::e6. doi: 10.3134/ehtj.10.006

Pandemic Preparedness Strategies for School Systems: Is Closure Really the Only Way?Lofgren, E., M. Senese, J. Rogers and N.H. Fefferman. 2008. Annales Zoologici Fennici, 45: 449-458.

Can Disease Models on Static Graphs Approximate Epidemics in Shifting Social Networks?Fefferman, N.H. and K.L. Ng. 2007. Physical Review E. 76:031919.

The Untapped Potential of Virtual Game Worlds to Shed Light on Real World Epidemics. Lofgren, E. and N.H. Fefferman. 2007. The Lancet Infectious Diseases. 7:625-629.

Influenza Seasonality: Underlying Causes and Modeling Theories Lofgren, E., N.H. Fefferman, Y.N. Naumov, J. Gorski and E.N. Naumova. 2007. Journal of Virology, 81(11):5429-5436.