### Abstract

Dengue epidemics, one of the most important viral disease worldwide, can be prevented by combating the transmission vector Aedes aegypti. In support of this aim, this article proposes to analyze the Dengue vector control problem in a multiobjective optimization approach, in which the intention is to minimize both social and economic costs, using a dynamic mathematical model representing the mosquitoes' population. It consists in finding optimal alternated step-size control policies combining chemical (via application of insecticides) and biological control (via insertion of sterile males produced by irradiation). All the optimal policies consists in apply insecticides just at the beginning of the season and, then, keep the mosquitoes in an acceptable level spreading into environment a few amount of sterile males. The optimization model analysis is driven by the use of genetic algorithms. Finally, it performs a statistic test showing that the multiobjective approach is effective in achieving the same effect of variations in the cost parameters. Then, using the proposed methodology, it is possible to find, in a single run, given a decision maker, the optimal number of days and the respective amounts in which each control strategy must be applied, according to the tradeoff between using more insecticide with less transmission mosquitoes or more sterile males with more transmission mosquitoes.

Original language | English |
---|---|

Pages (from-to) | 37-47 |

Number of pages | 11 |

Journal | Mathematical biosciences |

Volume | 269 |

Early online date | 8 Sep 2015 |

DOIs | |

Publication status | Published - Nov 2015 |

### Keywords

- Aedes
- algorithms
- Dengue
- insect vectors
- insecticides
- biological models
- mosquito control

## Fingerprint Dive into the research topics of 'A multiobjective optimization approach for combating Aedes aegypti using chemical and biological alternated step-size control'. Together they form a unique fingerprint.

## Profiles

## Elizabeth Wanner

- Engineering & Applied Science
- Computer Science - Lecturer in Computer Science

Person: Academic

## Cite this

*Mathematical biosciences*,

*269*, 37-47. https://doi.org/10.1016/j.mbs.2015.08.019