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Title: Mathematical modeling, analysis, and simulation of the COVID-19 pandemic with explicit and implicit behavioral changes
As COVID-19 cases continue to rise globally, many researchers have developed mathematical models to help capture the dynamics of the spread of COVID-19. Specifically, the compartmental SEIR model andits variations have been widely employed. These models differ in the type of compartments included, nature of the transmission rates, seasonality, and several other factors. Yet, while the spread of COVID-19 is largely attributed to a wide range of social behaviors in the population, several of these SEIR models do not account for such behaviors. In this project, we consider novel SEIR-based models that incorporate various behaviors. We created a baseline model and explored incorporating both explicit and implicit behavioral changes. Furthermore, using the Next Generation Matrix method, we derive a basic reproduction number, which indicates the estimated number of secondary cases by a single infected individual. Numerical simulations for the various models we made were performed and user-friendly graphical user interfaces were created. In the future, we plan to expand our project to account for the use of face masks, age-based behaviors and transmission rates, and mixing patterns.  more » « less
Award ID(s):
2031029
PAR ID:
10295704
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Computational and Mathematical Biophysics
Volume:
8
Issue:
1
ISSN:
2544-7297
Page Range / eLocation ID:
216-232
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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