Predictive approach of COVID-19 propagation via multipleterms sigmoidal transition model

作     者：Abdelbasset Bessadok-Jemai Abdulrahman A.Al-Rabiah 

作者机构：Chemical Engineering DepartmentCollege of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia 

出 版 物：《Infectious Disease Modelling》 (传染病建模（英文）)

年 卷 期：2022年第7卷第3期

页      面：387-399页

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：This studywas supported by King Saud University Deanship of Scientific Research College of Engineering Research Center 

主　　题：COVID-19 Pandemic SIR Logistic Sigmoidal transition Modeling 

摘      要：The COVID-19 pandemic with its new variants has severely affected the whole world socially and *** study presents a novel data analysis approach to predict the spread of *** and logistic models are commonly used to determine the duration at the end of the *** 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 *** this study,a multiple-term sigmoidal transition(MTST)model was developed and validated for several countries with multiple peaks and *** approach proved to fit the actual data better and allowed the spread of the pandemic to be accurately *** UK,Italy,Saudi Arabia,and Tunisia,which experienced several peaks of COVID-19,were used as case *** MTST model was validated for these countries for the data of more than 500 *** results show that the correlating model provided good fits with regression coefficients(R2)*** estimated model parameters were obtained with narrow 95%confidence interval *** has been found that the optimum number of terms to be used in the MTST model corresponds to the highest R^(2),the least RMSE,and the narrowest 95%confidence interval having positive bounds.