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A Novel Hybrid Modeling Method for Strain Evolution

Year: 2023

Presenter Name: Jude Horsley

Much is known about the progress of diseases in competition on a macroscopic scale. In general, competitive exclusion is the governing principle, so that the strain with the higher R value will spread more effectively and drive the other to extinction. However, this is not the full story. Viral mutations occur frequently due to the huge number of individual cells that exist within even a single body. It is therefore reasonable to question whether a viral strain might have to compete within the body with newly mutated strains. Each strain may have slightly different transmission parameters, as well as different parameters that dictate the progress of the infection within the body. These two parameters then evolve side-by-side. This work sought to create a model which took each of these factors into account while remaining realistic and produced results in keeping with observed data. In particular, it sought to determine whether mutant strains could coexist, or would necessarily exclude one another. A novel hybrid-style model was developed in R which explores the interplay between two strains of a virus-one which multiplies more quickly in the body, while the other is more effective at spreading between individuals. This was accomplished by blending stochastic and deterministic models. Within the body, the process of multiplication of virions was treated deterministically; whereas mutation of strains and person-to-person infection were treated as stochastic processes. Not only is this realistic, but it circumvents the computationally expensive pitfalls of fully stochastic agent-based models. The curves generated by the model take on a sigmoid shape which very closely resembles invasion curves observed during the initial advent of the Delta and Omicron strains of COVID-19 (see attached figure). Upon this success, the model was further modified to include a variable number of strains, with programmable mutation rates between each strain. This was once again checked against observed infection curves of known diseases, confirming that the model was consistent with reality. New work is now being done to model mutation through the construction of a virtual genome. We conclude that the model we created is a useful tool for investigating the evolution of multiple strains of a virus in competition. Some work has been done with this model in investigating the role of evolutionary valleys in delaying the evolution of new strains. Work is currently being done to investigate the model's implications for the coexistence of mutant strains. We continue to improve the model and find new implications for the development of rapidly evolving viruses.
University / Institution: University of Utah
Type: Oral
Format: In Person
SESSION B (10:45AM-12:15PM)
Area of Research: Science & Technology
Faculty Mentor: Frederick Adler
Location: Alumni House, HENRIKSEN ROOM (11:25am)