Discipline: Biological Sciences
Subcategory: Mathematics and Statistics
Room: Park Tower 8212
Kaeleen Boggs - Grand Canyon University
Co-Author(s): Lucero Urbieta, Arizona State University, Tempe Arizona
In 2014 the CDC reported that about 186 billion dollars was spent on health care services to treat mental health disorders. Depression has become an impact of the economical burden in the United States. Recent studies have stipulated an association between social media use and depression. We present an epidemiological model to investigate how various public health control strategies can influence the transmission dynamics of depression disorders among adolescents due to the use of social media. The model focuses on teens and the young adult population that are divided into four sub-classes: susceptible, lightly depressed, moderately depressed, severely depressed, and effectively treated. The next generation operator method is employed to identify the threshold value of this depression epidemic called the depression-generating number, RD. The analysis reveals that the number of depression cases reduces to the depression-free equilibrium whenever RD is less than one. Conversely, the number of depression cases will reach the depression persistent equilibrium when RD is greater than one. Different control strategies are considered to help minimize the impact of social media on depression: promoting treatment, self-counseling, public health counseling and a hybrid control strategy. Numerical analysis of the model indicates that the number of depression cases can be controlled via public health counseling or the hybrid strategy. Furthermore, a time delay of the depression epidemic is observed under the implementation of the hybrid control strategy. Further research will include the investigation of self-initiating depression cases, an extension of the model to include non-homogeneity in susceptibility, and possibilities of effectively-treated depressed individuals to relapse to any level of depression disorders.
Funder Acknowledgement(s): I thank to Dr. Abba Gumel and Dr. Terry L. Alford of Arizona State University (ASU) for the guidance during the program. I would also like to thank to the School for Engineering of Matter, Transport and Energy at ASU for providing access to resources and classroom. Funding was provided by an NSF HBCU-UP research initiation award (grant 074754805).
Faculty Advisor: Dr. Aprillya Lanz, email@example.com
Role: I participated in all aspects of the research.