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Modelling the impact of insecticide-based control interventions on the evolution of insecticide resistance and disease transmission

Overview of attention for article published in Parasites & Vectors, August 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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1 policy source
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1 X user

Citations

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23 Dimensions

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72 Mendeley
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Title
Modelling the impact of insecticide-based control interventions on the evolution of insecticide resistance and disease transmission
Published in
Parasites & Vectors, August 2018
DOI 10.1186/s13071-018-3025-z
Pubmed ID
Authors

Susana Barbosa, Katherine Kay, Nakul Chitnis, Ian M. Hastings

Abstract

Current strategies to control mosquito-transmitted infections use insecticides targeted at various stages of the mosquito life-cycle. Control is increasingly compromised by the evolution of insecticide resistance but there is little quantitative understanding of its impact on control effectiveness. We developed a computational approach that incorporates the stage-structured mosquito life-cycle and allows tracking of insecticide resistant genotypes. This approach makes it possible to simultaneously investigate: (i) the population dynamics of mosquitoes throughout their whole life-cycle; (ii) the impact of common vector control interventions on disease transmission; (iii) how these interventions drive the spread of insecticide resistance; and (iv) the impact of resistance once it has arisen and, in particular, whether it is sufficient for malaria transmission to resume. The model consists of a system of difference equations that tracks the immature (eggs, larvae and pupae) and adult stages, for males and females separately, and incorporates density-dependent regulation of mosquito larvae in breeding sites. We determined a threshold level of mosquitoes below which transmission of malaria is interrupted. It is based on a classic Ross-Macdonald derivation of the malaria basic reproductive number (R0) and may be used to assess the effectiveness of different control strategies in terms of whether they are likely to interrupt disease transmission. We simulated different scenarios of insecticide deployment by changing key parameters in the model to explore the comparative impact of insecticide treated nets, indoor residual spraying and larvicides. Our simulated results suggest that relatively low degrees of resistance (in terms of reduced mortality following insecticide contact) can induce failure of interventions, and the rate of spread of resistance is faster when insecticides target the larval stages. The optimal disease control strategy depends on vector species demography and local environmental conditions but, in our illustrative parametrisation, targeting larval stages achieved the greatest reduction of the adult population, followed by targeting of non-host-seeking females, as provided by indoor residual spraying. Our approach is designed to be flexible and easily generalizable to many scenarios using different calibrations and to diseases other than malaria.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 26%
Student > Ph. D. Student 10 14%
Student > Bachelor 7 10%
Student > Master 7 10%
Lecturer 2 3%
Other 5 7%
Unknown 22 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 21 29%
Medicine and Dentistry 7 10%
Biochemistry, Genetics and Molecular Biology 7 10%
Environmental Science 5 7%
Mathematics 2 3%
Other 7 10%
Unknown 23 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 January 2022.
All research outputs
#7,609,077
of 23,842,189 outputs
Outputs from Parasites & Vectors
#1,804
of 5,618 outputs
Outputs of similar age
#128,670
of 336,464 outputs
Outputs of similar age from Parasites & Vectors
#36
of 117 outputs
Altmetric has tracked 23,842,189 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 5,618 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 336,464 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.