Abstract

A mathematical model is proposed and analyzed for the understanding of growth pattern of mosquito vector looking into its life cycle. The objective of this study is to develop a mathematical model that can fit to the real data provided by DRDE scientist for different month at different stations so that the seasonal variation in population density of mosquitoes can be reported accurately to the estimated data obtained by the proposed mathematical model. The aquatic class $(L)$ and adult stage is divided in two class, indoor population $(I)$ and outdoor population $(O)$. Here we estimated different parameters of our proposed continuous model and numerically simulation is done to compare the estimated data with the original data.

Highlights

  • Vector-borne, mosquito-borne diseases, can be extremely fatal to human and animal populations

  • The aim of this paper is to develop a mathematical model to estimate and forecast mosquito population at different stations in accordance of the data provided by DRDE scientist to control vector population

  • We developed a mathematical model to study the growth pattern of mosquitoes on the basis of three classes, i.e., aquatic via two adult classes

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Summary

Introduction

Vector-borne, mosquito-borne diseases, can be extremely fatal to human and animal populations. The life cycle of mosquito involves aquatic(egg, larve and pupae) and terristrial (adult) stages and can be recognized by their special appearance. Mosquitoes lay their eggs on the surface of fresh water. When the development is complete the pupal skin split and the mosquito emerges as an adult and are commonly called "tumblers" and live in water from one to four days. As they are lighter than water floats at the surface. Keeping in view of the compartmental modelling approaches [6,7,13,14,15], in this paper, an attempt is made to develop a mathematical model from field data for mosquito population considering all the stage via aquatic and adult stage

Modelling Assumptions and a Generalized Conceptual Model
Least Square Curve Fitting of Time Series Data
Parameter Estimation and Mathematical Models
Conclusion

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