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Abstract

Many infectious disease models build upon the classic Susceptible-Infected-Recovered (SIR) model, a compartmental system that is used to simulate disease transmission in a population. The SIR model focuses on the transmission of disease but rarely includes behavioral or informational components that explore how disease perception influences transmission. In this paper, we propose a six-compartment behavioral SIR model that further segments the classic SIR system based on knowledge of information about the disease, and we explore how sharing information affects disease transmission. We designate two states as aware and unaware based on whether the relevant information is known by the population. Additionally, we include two types of information: good information that reduces transmission rates and bad information that increases transmission rates. We find that while compliance with good information is useful in decreasing community transmission, compliance with bad information has a greater magnitude of effect in terms of total cases. These results reaffirm that knowledge and human behavior are influential factors in disease transmission and should be included in future human disease models for more accurate transmission representation.

Author Bio

Katie Yan completed this work as a part of her senior thesis in mathematics at Skidmore College in 2021-2022. As an undergraduate Katie majored in both biology and mathematics with a strong passion for researching the intersection of the two disciplines via infectious disease modeling. Katie developed this interest from her freshman-year experience in London with Dr. Roe-Dale where she first learned about the plague and quarantine. Currently, Katie is a graduate student in Biology at Penn State University where she continues to study the intersections of disease, mathematical modeling, and information sharing.

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