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Background: Following the World Health Organization (WHO) endorsement of dual active ingredient (AI) nets, an increased uptake of pyrethroid-chlorfenapyr and pyrethroid-pyriproxyfen nets is expected. Studies evaluating their physical and insecticidal durability are essential for making programmatic and procurement decisions. This paper describes the methodology for a prospective study to evaluate the attrition, fabric integrity, insecticidal durability of Interceptor® G2 (alpha-cypermethrin-chlorfenapyr) and Royal Guard® (alpha-cypermethrin-pyriproxyfen), compared to Interceptor® (alpha-cypermethrin), embedded in a 3-arm cluster randomized controlled trial (cRCT) in the Zou Department of Benin. Methods: Ten clusters randomly selected from each arm of the cRCT will be used for the study. A total of 750 ITNs per type will be followed in 5 study clusters per arm to assess ITN attrition and fabric integrity at 6-, 12-, 24- and 36-months post distribution, using standard WHO procedures. A second cohort of 1800 nets per type will be withdrawn every 6 months from all 10 clusters per arm and assessed for chemical content and biological activity in laboratory bioassays at each time point. Alpha-cypermethrin bioefficacy in Interceptor® and Royal Guard® will be monitored in WHO cone bioassays and tunnel tests using the susceptible Anopheles gambiae Kisumu strain. The bioefficacy of the non-pyrethroid insecticides (chlorfenapyr in Interceptor® G2 and pyriproxyfen in Royal Guard®) will be monitored using the pyrethroid-resistant Anopheles coluzzii Akron strain. Chlorfenapyr activity will be assessed in tunnel tests while pyriproxyfen activity will be assessed in cone bioassays in terms of the reduction in fertility of blood-fed survivors observed by dissecting mosquito ovaries. Nets withdrawn at 12, 24 and 36 months will be tested in experimental hut trials within the cRCT study area against wild free-flying pyrethroid resistant An. gambiae sensu lato to investigate their superiority to Interceptor® and to compare them to ITNs washed 20 times for experimental hut evaluation studies. Mechanistic models will also be used to investigate whether entomological outcomes with each dual ITN type in experimental hut trials can predict their epidemiological performance in the cRCT. Conclusion: This study will provide information on the durability of two dual AI nets (Interceptor® G2 and Royal Guard®) in Benin and will help identify suitable methods for monitoring the durability of their insecticidal activity under operational conditions. The modelling component will determine the capacity of experimental hut trials to predict the epidemiological performance of dual AI nets across their lifespan. [ABSTRACT FROM AUTHOR]

Simple Summary: With resistance to the pyrethroid family of insecticides increasing, it is more important than ever that tools are available to measure the efficacy of alternatives. Pyriproxyfen (PPF) is an alternative insecticide whose mode of action sterilises adult mosquitoes. Consequently, the efficacy of PPF-based tools can be measured through visual examination of egg development by trained experts. This, however, can be a time-consuming process for which the required expertise can be difficult to train and is not available in many contexts. As such, we propose that an objective machine learning program, which can automatically classify the fertility status of adult mosquito ovaries via a colour image, be developed to improve the speed, accuracy, and consistency of assessment. This study shows that a convolutional neural network, built in Python's TensorFlow library, can quickly classify images of dissected ovaries into either 'fertile' or 'infertile' with a high accuracy rate. Such an application would be a practical and accessible tool available to all researchers studying the efficacy of PPF or other insecticides with a similar mode of action. Pyriproxyfen (PPF) may become an alternative insecticide for areas where pyrethroid-resistant vectors are prevalent. The efficacy of PPF can be assessed through the dissection and assessment of vector ovaries. However, this reliance on expertise is subject to limitations. We show here that these limitations can be overcome using a convolutional neural network (CNN) to automate the classification of egg development and thus fertility status. Using TensorFlow, a resnet-50 CNN was pretrained with the ImageNet dataset. This CNN architecture was then retrained using a novel dataset of 524 dissected ovary images from An. gambiae s.l. An. gambiae Akron, and An. funestus s.l., whose fertility status and PPF exposure were known. Data augmentation increased the training set to 6973 images. A test set of 157 images was used to measure accuracy. This CNN model achieved an accuracy score of 94%, and application took a mean time of 38.5 s. Such a CNN can achieve an acceptable level of precision in a quick, robust format and can be distributed in a practical, accessible, and free manner. Furthermore, this approach is useful for measuring the efficacy and durability of PPF treated bednets, and it is applicable to any PPF-treated tool or similarly acting insecticide. [ABSTRACT FROM AUTHOR]