A genetic model of the effects of insecticide-treated bed nets on the evolution of insecticide-resistance

Birget, Philip L. G.; Koella, Jacob C.

doi:10.1093/emph/eov019

Cited by 36 publications

(53 citation statements)

References 52 publications

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“…A "time to resistance" was calculated as the number of generations taken to reach a resistance allele frequency of 50%. This illustrates the changing selective pressure at each concentration and is compatible with previous analyses using this metric to quantify the rate of evolution of resistance (Birget & Koella, 2015;Levick et al, 2017;South & Hastings, 2018).…”

Section: Computer Simulation To Calculate Times To Resistance Thressupporting

confidence: 87%

“…To our knowledge, existing models of the evolution of insecticide resistance have not allowed inputs such as insecticide effectiveness and dominance to change over time (e.g. Barbosa et al, 2018;Birget & Koella, 2015;Levick et al, 2017;South & Hastings, 2018).…”

Section: Policy Implications and Conclusionmentioning

confidence: 99%

“…The selective advantage, shown in Figure 1c, operates over a single generation. The same pattern (although inverted) occurs when selection is compounded over generations in the computer simulation to estimate time to resistance.Figure 1dplots the time until resistance allele frequency reaches 50%, a conventional endpoint in many studies of insecticide resistance (e.g Birget & Koella, 2015)…”

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confidence: 99%

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The role of windows of selection and windows of dominance in the evolution of insecticide resistance in human disease vectors

South

Lees

Garrod

et al. 2019

Evolutionary Applications

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Persistent insecticides sprayed onto house walls, and incorporated into insecticide‐treated bednets, provide long‐acting, cost‐effective control of vector‐borne diseases such as malaria and leishmaniasis. The high concentrations that occur immediately postdeployment may kill both resistant and susceptible insects. However, insecticide concentration, and therefore killing ability, declines in the months after deployment. As concentrations decline, resistant insects start to survive, while susceptible insects are still killed. The period of time after deployment, within which the mortality of resistant individuals is lower than that of susceptible ones, has been termed the “window of selection” in other contexts. It is recognized as driving resistance in bacteria and malaria parasites, both of which are predominantly haploid. We argue that paying more attention to these mortality differences can help understand the evolution of insecticide resistance. Because insects are diploid, resistance encoded by single genes generates heterozygotes. This gives the potential for a narrower “window of dominance,” within the window of selection, where heterozygote mortality is lower than that of susceptible homozygotes. We explore the general properties of windows of selection and dominance in driving resistance. We quantify their likely effect using data from new laboratory experiments and published data from the laboratory and field. These windows can persist months or years after insecticide deployments. Differential mortalities of resistant, susceptible and heterozygous genotypes, after public health deployments, constitute a major challenge to controlling resistance. Greater attention to mortality differences by genotype would inform strategies to reduce the evolution of resistance to existing and new insecticides.

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Section: Computer Simulation To Calculate Times To Resistance Thressupporting

confidence: 87%

Section: Policy Implications and Conclusionmentioning

confidence: 99%

mentioning

confidence: 99%

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The role of windows of selection and windows of dominance in the evolution of insecticide resistance in human disease vectors

South

Lees

Garrod

et al. 2019

Evolutionary Applications

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“…Even so, it must be clear that these figures represented a major selection pressure for DDT and, more recently, pyrethroid resistance. Moreover, these numbers exclude insecticide use in the agricultural sector which most likely results in an additional intense selective pressure, particularly in the immature mosquito stages (Birget & Koella, ; Chouaïbou et al., ).…”

Section: Antimalarial Interventions and Their Evolutionary Consequencesmentioning

confidence: 99%

Putting evolution in elimination: Winning our ongoing battle with evolving malaria mosquitoes and parasites

Huijben

Paaijmans

2017

Evolutionary Applications

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Since 2000, the world has made significant progress in reducing malaria morbidity and mortality, and several countries in Africa, South America and South‐East Asia are working hard to eliminate the disease. These elimination efforts continue to rely heavily on antimalarial drugs and insecticide‐based interventions, which remain the cornerstones of malaria treatment and prevention. However, resistance has emerged against nearly every antimalarial drug and insecticide that is available. In this review we discuss the evolutionary consequences of the way we currently implement antimalarial interventions, which is leading to resistance and may ultimately lead to control failure, but also how evolutionary principles can be applied to extend the lifespan of current and novel interventions. A greater understanding of the general evolutionary principles that are at the core of emerging resistance is urgently needed if we are to develop improved resistance management strategies with the ultimate goal to achieve a malaria‐free world.

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“…Models have previously explored the impact of behavioral plasticity [35][36][37][38] and population genetics of insecticide resistance [39][40][41][42] among mosquitoes exposed to bednets. Some reassurance is gained from the current analysis which indicates that reductions in malaria are anticipated even in settings with high plasticity/resistance -a result that is not only corroborated by previous modeling studies but also the majority of empirical studies 43 .…”

Section: Discussionmentioning

confidence: 99%

Malaria control dynamics may explain inconsistent outcomes from bednet trials: a modeling study

Orsborne

Walker

Yakob

2018

Preprint

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BACKGROUND Long-lasting insecticidal bednets have unparalleled efficacy in reducing malaria burden. However, insecticidal resistance and bednet avoidance behaviors among the mosquito vectors are now widespread.METHODS Reviewing the relevant field and semi-field studies highlights the ubiquity of zoophagic and spatiotemporal (biting outdoors or at different times of day) plasticity among vectors in response to bednet deployment. Transmission models coupled with the population genetics of vectors are developed to assess the impact on malaria control caused by insecticide resistance and the avoidance behaviors of mosquitoes. RESULTS Interactions between physiological resistance and behavioral resilience among mosquitovectors can significantly impact malaria control efforts both in the short-and long-term. The possibility of misleading observations from injudiciously timed assessments of malaria control programs is demonstrated through simulation.CONCLUSIONS Currently, there are no guidelines to inform when during a bednet trial its effectiveness should be measured. The importance of this oversight is described in the context of recent randomized controlled bednet trials.

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A genetic model of the effects of insecticide-treated bed nets on the evolution of insecticide-resistance

Cited by 36 publications

References 52 publications

The role of windows of selection and windows of dominance in the evolution of insecticide resistance in human disease vectors

The role of windows of selection and windows of dominance in the evolution of insecticide resistance in human disease vectors

Putting evolution in elimination: Winning our ongoing battle with evolving malaria mosquitoes and parasites

Malaria control dynamics may explain inconsistent outcomes from bednet trials: a modeling study

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