Abstract Number:
3607
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Idamawatte Gedara Idamawatta (1), Bong-Jin Choi (1)
Institutions:
(1) North Dakota State University, Fargo,ND,USA
Co-Author:
First Author:
Presenting Author:
Abstract Text:
During the COVID-19 outbreak, the global community encountered numerous challenges, underscoring the necessity for effective prediction models to inform public health interventions and optimize resource allocation. Traditional compartmental models like the SIR (Susceptible-Infected-Recovered) model and its variants have been employed to predict disease prevalence. However, these models have limitations; they struggle to detect multiple waves and are sensitive to initial parameters, necessitating time-consuming parameter tuning. In this study, we propose an approach to identify multi-wave patterns in COVID-19 cases. Our method involves utilizing Bayesian changepoint detection to identify multiple waves, followed by the application of a logistic growth model to estimate daily COVID-19 cases, including hospitalizations and ICU patients. We evaluate the model's accuracy using Mean Absolute Percentage Errors (MAPE).
Keywords:
SIR model|Bayesian changepoint|Mean Absolute Percentage Errors| | |
Sponsors:
Section on Statistics in Epidemiology
Tracks:
Disease Prediction
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