The vector borne diseases share a high percentage of the annual deaths reported by the world health organization. Due to the involvement of multiple factors and resistance of the vector to the treatment strategies, it is highly desired to design a safe, efficient and harmless control strategy. A state of the art computational framework for the Wolbachia induced “genetic-control-strategy” has been documented during this research. The process of mating and molecular identification is complex and expensive and different labs around the world are devoted to format a smooth implementation strategy of this approach. During this research, as a first step, data was collected, secondly, dynamic artificial neural networks, with the “tapped delay” lines are used for nonlinear filtering of the data and for the prediction of the parametric values of the novel mathematical model. To study parametric reliance of stable solutions and unstable solution we analyze stability and the corresponding thresholds desired for this study are documented. The study conducted during this research can prove to be useful in future experimental and theoretical studies, aimed to control the vector borne diseases.

媒介传播的疾病在世界卫生组织报告的年度死亡中所占比例很高。由于多种因素的参与以及载体对治疗策略的抵抗性，因此迫切需要设计一种安全，有效且无害的控制策略。在这项研究中，已经记录了有关沃尔巴克氏菌诱发的“遗传控制策略”的最新计算框架。交配和分子鉴定的过程既复杂又昂贵，世界各地的不同实验室都致力于制定这种方法的平稳实施策略。在这项研究过程中，第一步是收集数据，其次是动态人工神经网络，带有“分接延迟”的线用于数据的非线性过滤和新型数学模型的参数值的预测。为了研究稳定解和不稳定解的参数依赖性，我们分析了稳定性，并记录了该研究所需的相应阈值。在此研究过程中进行的研究可证明对控制媒介传播疾病的未来实验和理论研究有用。