Abstract
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.