Predicting Multi-wave COVID-19 Cases Using Logistic Growth Modeling

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 

Section on Statistics in Epidemiology