Advanced Computing Approach for Modeling and Prediction COVID-19 Pandemic

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• The emergence of new coronavirus strains, like Omicron, necessitates robust methods for predicting COVID-19 (coronavirus disease 2019) spread.
• Accurate forecasting of COVID-19 cases is crucial for public health interventions and government decision-making.

Purpose of the Study:

• To develop and evaluate a statistical logistic growth model for predicting COVID-19 case numbers in Australia and Brazil.
• To assess the model's effectiveness in forecasting short-term (six-day) future case counts.

Main Methods:

• Utilized a validated logistic growth model applied to daily COVID-19 surveillance data from Australia and Brazil.
• Data spanned from March 13, 2020, to December 12, 2021 (641 days).
• Model performance was evaluated using the coefficient of determination (R²).

Main Results:

• The logistic growth model demonstrated high accuracy, achieving an R² value of 99% for predicting cumulative confirmed COVID-19 cases.
• The system showed robustness and effectiveness in capturing time-dependent dynamics of disease spread.
• Forecasts for future values within a six-day interval were reliable.

Conclusions:

• The proposed logistic growth model is a robust and effective system for predicting COVID-19 spread.
• The model's high accuracy supports its potential contribution to public health strategies for disease prevention.
• Accurate COVID-19 case prediction can inform timely public health interventions.