Bessadok-Jemai Abdelbasset, Al-Rabiah Abdulrahman A
Chemical Engineering Department, College of Engineering, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia.
Infect Dis Model. 2022 Sep;7(3):387-399. doi: 10.1016/j.idm.2022.06.008. Epub 2022 Jul 1.
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 R, the least RMSE, and the narrowest 95% confidence interval having positive bounds.
新冠疫情及其新变种在社会和经济方面给全球带来了严重影响。本研究提出了一种预测新冠疫情传播的新型数据分析方法。SIR模型和逻辑模型通常用于确定疫情结束时的持续时间。结果表明，对于疫情呈多峰和多波传播的国家，这些知名模型可能会给出不切实际的预测。一种基于S形转变（ST）模型的新预测方法比传统模型提供了更好的估计。在本研究中，针对几个疫情呈多峰和多波传播的国家，开发并验证了多阶S形转变（MTST）模型。该方法被证明能更好地拟合实际数据，并能准确追踪疫情的传播情况。以经历了新冠疫情多波高峰的英国、意大利、沙特阿拉伯和突尼斯作为案例研究。针对这些国家超过500天的数据对MTST模型进行了验证。结果表明，相关模型与回归系数（R2）>0.999拟合良好。估计的模型参数是在较窄的95%置信区间范围内获得的。研究发现，MTST模型中使用的最优项数对应着最高的R、最小的均方根误差（RMSE）以及具有正边界的最窄95%置信区间。
