Monday, Aug 5: 9:55 AM - 10:00 AM
3607
Contributed Speed
Oregon Convention Center
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).
SIR model
Bayesian changepoint
Mean Absolute Percentage Errors
Main Sponsor
Section on Statistics in Epidemiology