A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network  

作　　者：Zeshan Faiz Iftikhar Ahmed Dumitru Baleanu Shumaila Javeed 

机构地区：[1]Department of Mathematics,COMSATS University Islamabad,Islamabad,45550,Pakistan [2]Department of Mathematics,Cankaya University,Ankara,06790,Turkey [3]Institute of Space Sciences,Magurele-Bucharest,077125,Romania [4]Department of Medical Research,China Medical University Hospital,China Medical University,Taichung,40402,Taiwan [5]Department of Computer Science and Mathematics,Lebanese American University,Beirut,135053,Lebanon [6]Department of Mathematics,Mathematics Research Center,Near East University,Nicosia/Mersin,99138,Turkey

出　　处：《Computer Modeling in Engineering & Sciences》2024年第5期1217-1238,共22页工程与科学中的计算机建模（英文）

摘　　要：The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).

关 键 词：WOLBACHIA DENGUE neural network vertical transmission mean square error LEVENBERG-MARQUARDT 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]