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Mathematical modelling of vector-borne diseases and insecticide resistance evolution
BACKGROUND: Vector-borne diseases are important public health issues and, consequently, in silico models that simulate them can be useful. The susceptible-infected-recovered (SIR) model simulates the population dynamics of an epidemic and can be easily adapted to vector-borne diseases, whereas the H...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501426/ https://www.ncbi.nlm.nih.gov/pubmed/28694821 http://dx.doi.org/10.1186/s40409-017-0123-x |
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author | Gabriel Kuniyoshi, Maria Laura Pio dos Santos, Fernando Luiz |
author_facet | Gabriel Kuniyoshi, Maria Laura Pio dos Santos, Fernando Luiz |
author_sort | Gabriel Kuniyoshi, Maria Laura |
collection | PubMed |
description | BACKGROUND: Vector-borne diseases are important public health issues and, consequently, in silico models that simulate them can be useful. The susceptible-infected-recovered (SIR) model simulates the population dynamics of an epidemic and can be easily adapted to vector-borne diseases, whereas the Hardy-Weinberg model simulates allele frequencies and can be used to study insecticide resistance evolution. The aim of the present study is to develop a coupled system that unifies both models, therefore enabling the analysis of the effects of vector population genetics on the population dynamics of an epidemic. METHODS: Our model consists of an ordinary differential equation system. We considered the populations of susceptible, infected and recovered humans, as well as susceptible and infected vectors. Concerning these vectors, we considered a pair of alleles, with complete dominance interaction that determined the rate of mortality induced by insecticides. Thus, we were able to separate the vectors according to the genotype. We performed three numerical simulations of the model. In simulation one, both alleles conferred the same mortality rate values, therefore there was no resistant strain. In simulations two and three, the recessive and dominant alleles, respectively, conferred a lower mortality. RESULTS: Our numerical results show that the genetic composition of the vector population affects the dynamics of human diseases. We found that the absolute number of vectors and the proportion of infected vectors are smaller when there is no resistant strain, whilst the ratio of infected people is larger in the presence of insecticide-resistant vectors. The dynamics observed for infected humans in all simulations has a very similar shape to real epidemiological data. CONCLUSION: The population genetics of vectors can affect epidemiological dynamics, and the presence of insecticide-resistant strains can increase the number of infected people. Based on the present results, the model is a basis for development of other models and for investigating population dynamics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40409-017-0123-x) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5501426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55014262017-07-10 Mathematical modelling of vector-borne diseases and insecticide resistance evolution Gabriel Kuniyoshi, Maria Laura Pio dos Santos, Fernando Luiz J Venom Anim Toxins Incl Trop Dis Research BACKGROUND: Vector-borne diseases are important public health issues and, consequently, in silico models that simulate them can be useful. The susceptible-infected-recovered (SIR) model simulates the population dynamics of an epidemic and can be easily adapted to vector-borne diseases, whereas the Hardy-Weinberg model simulates allele frequencies and can be used to study insecticide resistance evolution. The aim of the present study is to develop a coupled system that unifies both models, therefore enabling the analysis of the effects of vector population genetics on the population dynamics of an epidemic. METHODS: Our model consists of an ordinary differential equation system. We considered the populations of susceptible, infected and recovered humans, as well as susceptible and infected vectors. Concerning these vectors, we considered a pair of alleles, with complete dominance interaction that determined the rate of mortality induced by insecticides. Thus, we were able to separate the vectors according to the genotype. We performed three numerical simulations of the model. In simulation one, both alleles conferred the same mortality rate values, therefore there was no resistant strain. In simulations two and three, the recessive and dominant alleles, respectively, conferred a lower mortality. RESULTS: Our numerical results show that the genetic composition of the vector population affects the dynamics of human diseases. We found that the absolute number of vectors and the proportion of infected vectors are smaller when there is no resistant strain, whilst the ratio of infected people is larger in the presence of insecticide-resistant vectors. The dynamics observed for infected humans in all simulations has a very similar shape to real epidemiological data. CONCLUSION: The population genetics of vectors can affect epidemiological dynamics, and the presence of insecticide-resistant strains can increase the number of infected people. Based on the present results, the model is a basis for development of other models and for investigating population dynamics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40409-017-0123-x) contains supplementary material, which is available to authorized users. BioMed Central 2017-07-06 /pmc/articles/PMC5501426/ /pubmed/28694821 http://dx.doi.org/10.1186/s40409-017-0123-x Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Gabriel Kuniyoshi, Maria Laura Pio dos Santos, Fernando Luiz Mathematical modelling of vector-borne diseases and insecticide resistance evolution |
title | Mathematical modelling of vector-borne diseases and insecticide resistance evolution |
title_full | Mathematical modelling of vector-borne diseases and insecticide resistance evolution |
title_fullStr | Mathematical modelling of vector-borne diseases and insecticide resistance evolution |
title_full_unstemmed | Mathematical modelling of vector-borne diseases and insecticide resistance evolution |
title_short | Mathematical modelling of vector-borne diseases and insecticide resistance evolution |
title_sort | mathematical modelling of vector-borne diseases and insecticide resistance evolution |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5501426/ https://www.ncbi.nlm.nih.gov/pubmed/28694821 http://dx.doi.org/10.1186/s40409-017-0123-x |
work_keys_str_mv | AT gabrielkuniyoshimarialaura mathematicalmodellingofvectorbornediseasesandinsecticideresistanceevolution AT piodossantosfernandoluiz mathematicalmodellingofvectorbornediseasesandinsecticideresistanceevolution |