Impact of Indoor Residual-Sprayed Deltamethrin on Different Surfaces in a Malaria Endemic Area in Balai Ringin, Sarawak

Malaria control programme utilizing indoor residual spraying of chemical insecticide is only effective if a high coverage of targeted area is achieved. The effectiveness of the residual spraying, on the other hand, relies on the efficacy and residual activity of the insecticides applied, which to a certain extent are influenced by the nature of the sprayed surfaces. The bioefficacy of indoor residual-sprayed deltamethrin wettable granule (WG) formulation for the control of malaria was compared with the current dose of deltamethrin wettable powder (WP) in malaria endemic areas in Balai Ringin, Sarawak. Doses of 20 mg/m2 WP (control), 20 mg/m2 WG, 30 mg/m2 WG and 40 mg/m2 WG were sprayed separately on different surfaces namely, wooden, rough-bamboo, smooth-bamboo and brick surfaces. Residual activity of WP and WG formulations was tested against lab-bred Anopheles maculatus using WHO standard procedure. Deltamethrin at 30 mg/m2 WG exhibited the highest sustainable level of effectiveness against An. maculatus (An. maculatus mortality was between 95% - 100%) up to week 60 post-spraying when sprayed on smooth- bamboo surface. These results indicated that 30 mg/m2 WG could be an ideal concentration for controlling malaria vector effectively up to 15 months of which long-lasting residual spraying was envisaged. The usual two spraying cycles per year with 20 mg/m2 deltamethrin WP could be replaced with 30 mg/m2 deltamethrin WG since the long residual activity was achieved by employing a single spraying only.

Nocturnal man biting habits of mosquito species in Serian, Sarawak, Malaysia

Knowledge of the composition and biting habits of mosquito associated with endemic areas is important in establishing sound vector control programmes and understanding the epidemiology of vector borne diseases. The biting activity cycle of several mosquito species in Serian, Sarawak was observed and described. Collections were carried out indoors and outdoors for 12 hours from 6.00 pm to 6.00 am using human landing catch techniques. A total of 7271 mosquitoes comprising 27 species belonging to sixgenera were collected. Mansonia bonneae (23.6%) was the predominant species caught in the study areas followed by Culex vishnui (22.3%), Culex pseudovishnui (19.6%) and Culex tritaenorhynchus (13.7%). A high rate of human biting activity by Ma. bonneae was detected during November but the activity was low during January.The biting activity of Ma.bonneae was found higher outdoor compared to indoorand peaked at 7.00 pm-8.00 pm.Cx.vishnui also exhibited similar biting activity peak while Anopheles letiferex-hibited biting activity peaked at 12.00 am-1.00 am.Cx.pseudovishnui showed biting patterns which were almost similar between indoor and outdoor activity and could be considered active throughout the 12 hour period.

The practicality of Malaysia dengue outbreak forecasting model as an early warning system

Dengue is a harmful tropical disease that causes death to many people.Currently,the dengue vaccine development is still at an early stage,and only intervention methods exist after dengue cases increase.Thus,previously,two scientific experimental field studies were conducted in producing a dengue outbreak forecasting model as an early warning system.Successfully,an Autoregressive Distributed Lag(ADL)Model was developed using three factors:the epidemiological,entomological,and environmental with an accuracy of 85%;but a higher percentage is required in minimizing the error for the model to be useful.Hence,this study aimed to develop a practical and cost-effective dengue outbreak forecasting model with at least 90%accuracy to be embedded in an early warning computer system using the Internet of Things(IoT)approach.Eighty-one weeks of time series data of the three factors were used in six forecasting models,which were Autoregressive Distributed Lag(ADL),Hierarchical Forecasting(Bottom-up and Optimal combination)and three Machine Learning methods:(Artificial Neural Network(ANN),Support Vector Machine(SVM)and Random Forest).Five error measures were used to evaluate the consistency performance of the models in order to ensure model performance.The findings indicated Random Forest outperformed the other models with an accuracy of 95%when including all three factors.But practically,collecting mosquito related data(the entomological factor)was very costly and time consuming.Thus,it was removed from the model,and the accuracy dropped to 92%but still high enough to be of practical use,i.e.,beyond 90%.However,the practical ground operationalization of the early warning system also requires several rain gauges to be located at the dengue hot spots due to localized rainfall.Hence,further analysis was conducted in determining the location of the rain gauges.This has led to the recommendation that the rain gauges should be located about 3e4 km apart at the dengue hot spots to ensure the accuracy of the rainfall data to be included in the dengue outbreak forecasting model so that it can be embedded in the early warning system.Therefore,this early warning system can save lives,and prevention is better than cure.