A backbone of traditional epidemiology is the Susceptible-Infected-Recovered (SIR) model for infectious disease spread. SIR models allow for the mathematical depiction and interpretation of infection dynamics with three general parameters of susceptible, infected, and recovered individuals for a given disease system. While SIR models are a unique and commonly used type of infectious disease modeling, uncertainty is almost always present in determining optimal control of pandemics. At the initial outbreak of a disease, disease rates--including transmission rate, recovery rate, death rate, and host arrival rate--are often unknown, making it difficult for researchers to understand the ideal end results. Consequently, then public health and medical officials are unable to make informed decisions on treatment options and safety measures to put in place to ensure the health of the affected populations. There has been a push to predict ideal disease rates associated with a specific system, allowing researchers to devise proper public health protocols to achieve these rates and the highest amounts of recovered individuals for an outbreak. With a clear indication of how a disease is transmitted, how individuals in a population interact, and how hosts recover/suffer from infection and proper modeling, interventions can combat disease outbreaks, epidemics, and pandemics. As it is difficult to create SIR models with optimal disease transmission, recovery, death, and host arrival rates, PandemicPriority is a generalized tool which computationally determines optimal values for each of these rates through randomized search for the future development of infectious disease treatments and interventions.
This talk will outline my journey as a student in Biology and Computer Science while I was struggling to combine my passions in both fields. How is it that Python can connect to traditional disease modeling? PandemicPriority gave me the opportunity to understand the complexities of Python programming for optimizing disease models, paving the way towards my interest in computational epidemiology.