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NIMBioS Postdoctoral Fellow Profile: Suzanne O'Regan

Q&A with Dr. O'Regan

S. O'Regan photo. As a postdoctoral fellow at NIMBioS, Suzanne O'Regan's research involves developing mathematical models to better understand the critical transitions related to the emergence and elimination of infectious disease. Her work has already lead to the development of the first theory of early warning signals for emergence and leading indicators of elimination of directly transmissible acute infections. In this Q&A from NIMBioS, Dr. O'Regan tells us what inspires her and what she enjoys doing when not engaged in her research.

Hometown: Cork, Ireland
Field of Study: Mathematical Biology

What is your field and why did you choose it?

I am an applied mathematician who specializes in mathematical biology. Most of my work involves building and analyzing mathematical models of infectious diseases. I chose to work in this field because the questions it tries to answer have implications for human health and wellbeing. These questions include: What causes an epidemic? Can we forecast how fast a disease spreads? How large will an outbreak be? What factors lead to sustained transmission of disease in a population? Will a pathogen persist or stutter to extinction? Can we use the knowledge gained from modeling to prevent future disease outbreaks? Is disease emergence preceded by signatures that can be detected from clinical case reports? I make efforts to incorporate relevant aspects of ecology and epidemiology in my models. I use the models to gain insight into the effects of underlying processes on infectious disease outbreaks.

Describe your current research.

Many complex systems, for example, financial markets, ecosystems, physiological systems and the climate, have "tipping points" at which a sudden shift from one dynamical state to another may occur. For example, ecosystem collapse represents a tipping point being crossed. Giving the earliest possible warning for such drastic events, by detecting these tipping points before they occur, is of immense practical interest. Work in a variety of scientific fields suggests the existence of early warning signals that might indicate that a critical transition is being approached for a wide class of systems. Infectious disease systems are also complex and have tipping points. An approach that I am developing for epidemic forecasting is based on techniques developed by ecologists for predicting sudden changes in the state of lakes and other ecosystems. Potentially, these early warning systems for infectious diseases could prevent morbidity and mortality from disease outbreaks. They might also motivate policymakers to continue to promote control and prevention strategies to sustain the gains made from disease elimination campaigns.

What is the primary aim of your research?

The primary aim of my research is to understand the patterns of emergence and elimination of infectious disease using mathematical models. Emergence and elimination of infectious diseases are critical transitions. A critical point is crossed when a disease emerges and its transmission becomes sustained, leading to a rapid increase in cases. A critical threshold is also crossed when the number of cases of a disease is reduced as a result of control efforts to the point where transmission is no longer sustained and the disease is eventually eliminated from the population. Understanding the factors and processes that lead to these dynamics could be used to signal an early warning of emergence events or indicate if disease elimination is imminent. 

What I am interested in, more generally, is detection of critical transitions in disease systems before they occur. A dynamical phenomenon known as critical slowing down may be detectable in scenarios where the underlying epidemiological processes are gradually changing in time. Examples of gradually changing processes that may lead to a critical transition in an infectious disease include evolution of the pathogen, variation in host behavior, changes in the fraction of people vaccinated, or shifts in climate that may affect vector population abundances. Signatures of critical slowing down may be detectable in time series of clinical case reports, through measurement of statistics such as variance, autocorrelation and the power spectrum. My work has used simple mathematical models to calculate these statistics. I am interested in continuing to make use of the phenomenon of critical slowing down as a means of detecting the onset of emergence and elimination of infectious diseases. My goal going forward at NIMBioS is to build seasonal and spatial variation into my models and to use them to develop a theoretical framework and test bed for early warning systems for infectious disease outbreaks.

How does your work benefit society?

Transmissible pathogens continue to pose a major threat to human health. The global morbidity and mortality burden from infectious diseases remains extremely high, and the possibilities for emergence and spread of diseases in the coming decades are likely to increase as a result of population growth, increased urbanization, greater travel, and increased land use to meet demands from the world's growing population. The dynamics of infectious diseases near tipping points are therefore of tremendous practical interest. Infectious disease forecasting requires mathematical tools for anticipating emergence and elimination before they occur.  My research will hopefully lead to methods and algorithms that will help world health systems to be more prepared for epidemics and will prevent deaths from disease.

What do you like best about your work?

There is a perception that mathematicians work in isolation, but it is essential for mathematical modellers like myself to communicate effectively with their colleagues in other scientific disciplines. This part of the work brings me great joy. I enjoy sharing ideas and working with others toward a common goal. On a day-to-day basis, the teaching, mentoring and outreach work that I do allows me to share my enthusiasm for mathematical modelling with others and to communicate that mathematical tools can be used to address important questions for human health. Finally, it is a joy and a privilege to go to work and learn every day. I enjoy learning the mathematical and computational approaches that I need to solve a problem. I am glad to be able to say that the research I do may some day improve the quality of people's lives.

What would your Tweet say about your work? What would your elevator speech say?

My tweet:
NIMBioS postdoc |  Mathematical modeller | Applied mathematician fascinated by the ecology of infectious diseases and mathematical ecology.

I am a mathematical modeller of infectious diseases. I am interested in developing mathematical tools for early warning systems that can be applied as adaptive management in response to disease outbreaks. I am developing approaches for epidemic forecasting that are based on techniques developed by ecologists for predicting sudden changes in the state of lakes and other ecosystems.

Which professional accomplishment are you most proud of?

I am proud that I developed the first theory of early warning signals for emergence and leading indicators of elimination of directly transmissible acute infections. This theory predicts that pathogens on the brink of emergence or elimination will exhibit a dynamical phenomenon called critical slowing down. I calculated early warning statistics for anticipation of disease emergence and elimination and tested their performance on simulated data.

On the other hand, what has been your most discouraging professional moment and how did you recover? What did you learn?

Getting your work rejected by your peers is always hard but is a fact of life for all scientists. Every time it happens I see it as an opportunity to improve my science and become a better communicator of my work.

What is the most surprising aspect of your work?

I am continually surprised by the complexity of the interactions between hosts and parasites and heterogeneities in host attributes such as age, spatial location and infection risk factors that affect disease dynamics. Non-random risk and contact structures, local and long-range transmission and the existence of multiple animal hosts and zoonotic reservoirs are all examples of complexities that can lead to surprising transmission outcomes. Such complexities may need to be quantified to assess the risk of disease spread and the potential impact of interventions because they impact transmission processes, which are inherently nonlinear. The recent Ebola epidemic in West Africa and recent upsurges in cases of measles and polio despite global elimination efforts are examples. These complexities underscore how necessary it is to use models to provide the quantitative insights into counterintuitive infectious disease dynamics. It is an important challenge for mathematical modellers to usefully incorporate them into models in order to make careful predictions.

What do you do when you're not in the lab or out in the field?

I enjoy the outdoors and I am a keen hiker. I have been exploring the Smokies since I have arrived in Knoxville.

Suzanne O'Regan's Postdoc page

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NSF logo. NIMBioS is sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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