Predictive approach of COVID-19 propagation via multiple-terms sigmoidal transition model

Abstract

The COVID-19 pandemic with its new variants has severely affected the whole world socially and economically. This study presents a novel data analysis approach to predict the spread of COVID-19. SIR and logistic models are commonly used to determine the duration at the end of the pandemic. Results show that these well-known models may provide unrealistic predictions for countries that have pandemics spread with multiple peaks and waves. A new prediction approach based on the sigmoidal transition (ST) model provided better estimates than the traditional models. In this study, a multiple-term sigmoidal transition (MTST) model was developed and validated for several countries with multiple peaks and waves. This approach proved to fit the actual data better and allowed the spread of the pandemic to be accurately tracked. The UK, Italy, Saudi Arabia, and Tunisia, which experienced several peaks of COVID-19, were used as case studies. The MTST model was validated for these countries for the data of more than 500 days. The results show that the correlating model provided good fits with regression coefficients (R2) > 0.999. The estimated model parameters were obtained with narrow 95% confidence interval bounds. It has been found that the optimum number of terms to be used in the MTST model corresponds to the highest R2, the least RMSE, and the narrowest 95% confidence interval having positive bounds.

Keywords

• COVID-19
• Pandemic
• SIR
• Logistic
• Sigmoidal transition
• Modeling