Gravid oviposition sticky trap and dengue non-structural 1 antigen test for early surveillance of dengue in multi-storey dwellings: study protocol of a cluster randomized controlled trial

Background:Dengue is a global disease,transmitted by the Aedes vectors.In 2018,there were 80615 dengue cases with 147 deaths in Malaysia.Currently,the nationwide surveillance programs are dependent on Aedes larval surveys and notifications of lab-confirmed human infections.The existing,reactive programs appear to lack sensitivity and proactivity.More efficient dengue vector surveillance/control methods are needed.Methods:A parallel,cluster,randomized controlled,interventional trial is being conducted for 18 months in Damansara Damai,Selangor,Malaysia,to determine the efficacy of using gravid oviposition sticky(GOS)trap and dengue non-structural 1(NS1)antigen test for early surveillance of dengue among Aedes mosquitoes to reduce dengue outbreaks.Eight residential apartments were randomly assigned into intervention and control arms.GOS traps are set at the apartments to collect Aedes weekly,following which dengue NS1 antigen is deteaed in these mosquitoes.When a dengue-positive mosquito is detected,the community will be advised to execute vector search-and-destroy and protective measures.The primary outcome concerns the the percentage change in the(i)number of dengue cases and(ii)durations of dengue outbreaks.Whereas other outcome measures include the change in density threshold of Aedes and changes in dengue-related knowledge,attitude and practice among cluster inhabitants.Discussion:This is a proactive and early dengue surveillance in the mosquito vector that does not rely on notification of dengue cases.Surveillance using the GOS traps should be able to efficiently provide sufficient coverage for multistorey dwellings where population per unit area is likely to be higher.Furthermore,trapping dengue-infected mosquitoes using the GOS trap,helps to halt the dengue transmission carried by the mosquito.It is envisaged that the results of this randomized controlled trial will provide a new proactive,cheap and targeted surveillance tool for the prevention and control of dengue outbreaks.Trial registration:This is a parallel-cluster,randomized controlled,interventional trial,registered at ClinicalTrials.gov(ID:NCT03799237),on 8th January 2019(retrospectively registered).

Automatic greenhouse pest recognition based on multiple color space features

Recognition and counting of greenhouse pests are important for monitoring and forecasting pest population dynamics.This study used image processing techniques to recognize and count whiteflies and thrips on a sticky trap located in a greenhouse environment.The digital images of sticky traps were collected using an image-acquisition system under different greenhouse conditions.If a single color space is used,it is difficult to segment the small pests correctly because of the detrimental effects of non-uniform illumination in complex scenarios.Therefore,a method that first segments object pests in two color spaces using the Prewitt operator in I component of the hue-saturation-intensity(HSI)color space and the Canny operator in the B component of the Lab color space was proposed.Then,the segmented results for the two-color spaces were summed and achieved 91.57%segmentation accuracy.Next,because different features of pests contribute differently to the classification of pest species,the study extracted multiple features(e.g.,color and shape features)in different color spaces for each segmented pest region to improve the recognition performance.Twenty decision trees were used to form a strong ensemble learning classifier that used a majority voting mechanism and obtains 95.73%recognition accuracy.The proposed method is a feasible and effective way to process greenhouse pest images.The system accurately recognized and counted pests in sticky trap images captured under real greenhouse conditions.