Modelling New Insecticide-Treated Bed-Nets for Malaria-Vector Control: How to Strategically Manage Resistance?

Madgwick, Philip G.; Kanitz, Ricardo

doi:10.21203/rs.3.rs-721220/v1

Cited by 1 publication

(15 citation statements)

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“…This reveals that, for sequences, the time to resistance increases with lower exposure and effectiveness in a negative exponential‐like relationship, whereas for mixtures there is a similar relationship except that the time to resistance also increases at higher effectiveness to give a U‐like relationship across effectiveness. Both these relationships are well‐known from previous modelling work 28,42,43 …”

Section: How Do Mixtures Work?supporting

confidence: 53%

“…Second, the theoretical description of the differential effects of mixtures would need to be bolstered to explain the phenomena in experimental data. Recent modelling has made some progress, [41][42][43][44][45][46] but the comparisons between mixtures and other strategies have tended to be simplistic and, for example, leave the benefits of additional kill unaccounted for. Here, the study of insecticide mixtures could benefit from comparisons with the parallel literature on fungicide mixtures, 47,[51][52][53] which are commonly used for resistance management.…”

Section: Future Directionsmentioning

confidence: 99%

“…This becomes apparent in the discussion of the 'soft fail' of mixtures (i.e. performing at least as well as sequences), where misunderstandings have arisen around the importance of resistance being recessive and initially rare to the absolute 31,36 and relative 22,37 change in the time to resistance, which can lead to a much longer absolute time to resistance for mixtures but little increase in the relative time to resistance in comparison to sequences (see also 42,43 ). For discussing when mixtures work by doing something different compared to other strategies, the focus must be on the conditions that lead to a relative advantage of mixtures over other strategies.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a series of new modelling papers have challenged this consensus, 41–46 although these studies have not been framed in these terms. One recent simulation study of insecticides for vector control used massive simulations and a powerful machine learning analysis to address how variation in parameter values contributes toward mixtures being favoured over sequences 42 .…”

Section: Introductionmentioning

confidence: 99%

“…Interestingly, this explanation makes little sense of the ‘secondary’ role of lower exposure, which was left unexplained. A different study, using a very similar model and machine learning approach but running many more simulations, compared two kinds of mixtures to sequences 43 . Mixtures of the kind from the previous study, with insecticides that have the same effectiveness in mixtures as in sequences, performed similarly to in the previous study.…”

Section: Introductionmentioning

confidence: 99%

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Beyond redundant kill: A fundamental explanation of how insecticide mixtures work for resistance management

Madgwick

Kanitz

2022

Pest Management Science

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The use of insecticide mixtures for resistance management has been a controversial topic for many decades. Here, we provide a reassessment of the fundamental theory of insecticide mixtures. First, we examine how mixtures differ from other strategies. We suggest that the fundamental strategy concept of a mixture is defined by the simultaneous use of insecticides and their overlapping exposure. Second, we provide a simple, illustrative model to show how mixtures affect resistance evolution. Following the existing literature, we identify a role for ‘redundant kill’ acting against resistant individuals, which we link to the overlapping exposure of insecticides. We also identify the occurrence of ‘additional kill’ acting against susceptible individuals, which is the immediate consequence of the simultaneous use of insecticides. Third, we take a basic approach to the comparison of mixtures and other strategies using a simple model. We find that a common comparison of the time to resistance alone leaves the effects of additional kill unaccounted for. Moreover, we demonstrate that different approaches to comparison can lead to different results because of biases that are introduced in the comparison setup. Fourth, still using the same model, we showcase a more sophisticated approach to comparison using optimised strategies. We find that optimised mixtures always perform better than other strategies due to the combination of redundant and additional kill. We suggest that the comparison of optimised strategies is unbiased because each strategy is performing the best that it can. On this basis, in theory (but not necessarily practice), we believe that mixtures are better than other strategies and, through the steps of our argument, we can tie this success back to the fundamental properties (of simultaneous use and overlapping exposure) that distinguish mixtures from other strategy concepts. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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Section: How Do Mixtures Work?supporting

confidence: 53%