Christina Edholm, Scripps College – Traveling and Disease Outbreaks
On Scripps College Week: Gaining more information on disease outbreaks will be crucial for the future.
Christina Edholm, assistant professor of mathematics, discusses how managers can better prepare for these epidemics.
Christina Edholm, Ph.D., is an Assistant Professor in Mathematics at Scripps College in Claremont, California. Her area of expertise is applied mathematics, with specific focus on mathematical biology, including management of invasive species using control theory and epidemiological modeling. She has participated in the Masamu mathematics program since 2014 and has attended the Southern Africa Mathematical Science Association conference, investigating what kind of role healthcare education plays in outbreaks and how education campaigns could help manage future outbreaks. She holds a doctorate in mathematics from the University of Nebraska.
Traveling and Disease Outbreaks
When you turn on the news, you hear about a disease outbreak or an ongoing epidemiological event. People traveling need to consider what diseases are present in different locations. By using mathematical models, we can gain insight into different questions about diseases and outbreaks. We can also predict possible management options to change the course of an outbreak, and prevent future cases.
My research focuses on many different dynamics for a variety of diseases. I started modeling outbreaks in 2014 with Ebola Virus Disease, trying to discern why the number of cases during the 2014 outbreak was so much larger than any previous time. We focused on formulating a differential equation model that incorporated the population dynamics specific to cultural differences. Additionally, we considered how an education campaign can affect an outbreak by separating the population and including a parameter for increasing or decreasing disease education. Through this model we studied historical Ebola outbreaks in Sudan and observed the effects increasing an education campaign during and prior to an outbreak can have. A key feature of this model was capturing people’s behavior with respect to an education campaign, and studying the different transmission dynamics.
I also study the effects of superspreaders, individuals in the population who transmit disease at a disproportionately higher rate. We are studying a variety of models to identify characteristics of superspreaders in an outbreak, which could help reduce the number of overall infections. For analysis, we use different mathematical techniques along with computational simulations. Currently we are studying superspreaders in Middle Eastern Respiratory Syndrome, Ebola and Coronavirus disease outbreaks. Some of the key factors we consider are time to peak infection, size of peak infection, time to an outbreak, number of deaths, and the probability of an outbreak.
We hope to gain more insight into disease dynamics and outbreaks, leading to management solutions.