| Abstract: |
| The incorporation of human behavior into epidemiological modeling has been quite a popular topic as of late, and there are many effective ways to mathematically introduce behavior into such models. In this talk, we incorporate behavior into an epidemiological model for an upper respiratory infectious disease based upon an assumption that the sicker an individual feels the more likely they will reduce their contacts. Our methodology in capturing this behavioral assumption will be through a nested-multiscale modeling approach. The within-host dynamics of a symptomatic individual is represented by a system of ordinary differential equations in which one of the compartments measures the symptom score of the infected individual. This model is then linked to an age-structured between-host model (where age represents time since infection) by having a reduction in contact rate be a function that depends on the individual`s symptom score. In this talk, we present a flowchart of the model, some of the analytical results, and some simulations. In our simulations, we vary parameters of the linking function to investigate how the transmission dynamics change based on how reactive an individual is to their symptoms. |
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