Genetic algorithm for optimization of the aedes aegypti control strategies

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dc.contributor.authorFlorentino, Helenice O. [UNESP]
dc.contributor.authorCantane, Daniela R. [UNESP]
dc.contributor.authorSantos, Fernando L.P. [UNESP]
dc.contributor.authorReis, Célia A. [UNESP]
dc.contributor.authorPato, Margarida V.
dc.contributor.authorJones, Dylan
dc.contributor.authorCerasuolo, Marianna
dc.contributor.authorOliveira, Rogério A. [UNESP]
dc.contributor.authorLyra, Luiz G. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de Lisboa
dc.contributor.institutionUniversity of Portsmouth
dc.date.accessioned2019-10-06T16:15:02Z
dc.date.available2019-10-06T16:15:02Z
dc.date.issued2018-09-01
dc.description.abstractDengue Fever, Zika and Chikungunya are febrile infectious diseases transmitted by the Aedes species of mosquito with a high rate of mortality. The most common vector is Aedes aegypti. According to World Health Organization outbreaks of mosquito-borne illnesses are common in the tropical and subtropical climates, as there are currently no vaccines to protect against Dengue Fever, Chikungunya or Zika diseases. Hence, mosquito control is the only known method to protect human populations. Consequently, the affected countries need urgently search for better tools and sustained control interventions in order to stop the growing spread of the vector. This study presents an optimization model, involving chemical, biological and physical control decisions that can be applied to fight against the Aedes mosquito. To determine solutions for the optimization problem a genetic heuristic is proposed. Through the computational experiments, the algorithm shows considerable efficiency in achieving solutions that can support decision makers in controlling the mosquito population.en
dc.description.affiliationDepartamento de Bioestatística – IB UNESP, Bairro Rubião Júnior
dc.description.affiliationDepartamento de Matemática – FC UNESP
dc.description.affiliationISEG and CMAFCIO Universidade de Lisboa
dc.description.affiliationCentre for Operational Research and Logistics University of Portsmouth
dc.description.affiliationDepartment of Mathematics University of Portsmouth
dc.description.affiliationUnespDepartamento de Bioestatística – IB UNESP, Bairro Rubião Júnior
dc.description.affiliationUnespDepartamento de Matemática – FC UNESP
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipFundação para o Desenvolvimento da UNESP (FUNDUNESP)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação para a Ciência e a Tecnologia
dc.description.sponsorshipUniversidade Estadual Paulista
dc.description.sponsorshipIdFUNDUNESP: 0351/019/13
dc.description.sponsorshipIdFAPESP: 2009/14901-4
dc.description.sponsorshipIdFAPESP: 2009/15098-0
dc.description.sponsorshipIdFAPESP: 2010/07585-6
dc.description.sponsorshipIdFAPESP: 2014/01604-0
dc.description.sponsorshipIdCNPq: 302454/2016-0
dc.format.extent389-411
dc.identifierhttp://dx.doi.org/10.1590/0101-7438.2018.038.03.0389
dc.identifier.citationPesquisa Operacional, v. 38, n. 3, p. 389-411, 2018.
dc.identifier.doi10.1590/0101-7438.2018.038.03.0389
dc.identifier.fileS0101-74382018000300389.pdf
dc.identifier.issn1678-5142
dc.identifier.issn0101-7438
dc.identifier.scieloS0101-74382018000300389
dc.identifier.scopus2-s2.0-85060399899
dc.identifier.urihttp://hdl.handle.net/11449/188657
dc.language.isoeng
dc.relation.ispartofPesquisa Operacional
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectGenetic algorithms
dc.subjectHealthcare operational research
dc.subjectOptimization models
dc.titleGenetic algorithm for optimization of the aedes aegypti control strategiesen
dc.typeArtigo
unesp.author.lattes7644869884732752[4]
unesp.author.orcid0000-0003-0981-7001[4]

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