COMPUTATIONAL SOCIAL SCIENCE

Department of Computational Social Science Seminar-CHOPRA

Friday, November 8, 3:00 p.m.
Center for Social Complexity Suite
Research Hall, Third Floor

An Agent-based Model for the Outbreak, Spread and Containment of Tuberculosis

Parth Chopra, Student
Thomas Jefferson High School for Science and Technology

ABSTRACT: Tuberculosis (TB) is the second most common form of death from an infectious disease, but yet it is still unknown exactly how it outbreaks and spreads within a population. An ABM, with humans as agents, was created and applied to the TB problem to see what epidemiological dynamics may occur, and what could be learned about the disease. It was coded in MASON, and an SEIR (Susceptible-Exposed-Infectious-Recovered) submodel was developed specifically for TB progression. The slum of Kibera, Kenya (the largest urban slum in Africa, and an area where TB and HIV is particularly rampant) was chosen as a test-case, and its geospatial and demographic information was used for calibration. The model successfully went through VV&T (Verification, Validation and Testing) using established techniques of Qualitative Agreement, Face Validation, and Extreme Input Testing. Preliminary results obtained from standard model runs show that TB epidemics progress in staircase patterns of emergence and stabilization. Furthermore, it was found that TB was creating static hotspots, or pockets of dense disease concentration, from where it was spreading. The results and lessons gleaned from the model can be easily incorporated into current health policies to mitigate TB’s negative impact. Lastly, the research shows the potential of ABMs in investigating infectious diseases.