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

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:

Bong-Jin Choi  
North Dakota State University

First Author:

Idamawatte Gedara Idamawatta  
North Dakota State University

Presenting Author:

Idamawatte Gedara Idamawatta  
N/A

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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