Speaker
Description
The incorporation of human behavior into epidemiological models 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 the following assumption: “The more sick a person is 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 ODEs in which one of the compartments measures the symptom score of the infected individual. This model is then linked to an aged-structured between-host model (where age represents time since infection) by having a reduction in contact rate of symptomatic individuals 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 spread of the infection changes based on how reactive an individual is to their symptoms.
