METHODS: A randomized 4 × 4 Latin square designed experiment was conducted to compare the efficiency of the Mosquito Magnet against three other common trapping methods: human landing catch (HLC), CDC light trap and human baited trap (HBT). The experiment was conducted over six replicates where sampling within each replicate was carried out for 4 consecutive nights. An additional 4 nights of sampling was used to further evaluate the Mosquito Magnet against the "gold standard" HLC. The abundance of Anopheles sampled by different methods was compared and evaluated with focus on the Anopheles from the Leucosphyrus group, the vectors of knowlesi malaria.
RESULTS: The Latin square designed experiment showed HLC caught the greatest number of Anopheles mosquitoes (n = 321) compared to the HBT (n = 87), Mosquito Magnet (n = 58) and CDC light trap (n = 13). The GLMM analysis showed that the HLC method caught significantly more Anopheles mosquitoes compared to Mosquito Magnet (P = 0.049). However, there was no significant difference in mean nightly catch of Anopheles mosquitoes between Mosquito Magnet and the other two trapping methods, HBT (P = 0.646) and CDC light traps (P = 0.197). The mean nightly catch for both An. introlatus (9.33 ± 4.341) and An. cracens (4.00 ± 2.273) caught using HLC was higher than that of Mosquito Magnet, though the differences were not statistically significant (P > 0.05). This is in contrast to the mean nightly catch of An. sinensis (15.75 ± 5.640) and An. maculatus (15.78 ± 3.479) where HLC showed significantly more mosquito catches compared to Mosquito Magnet (P
METHODS: Vector data from various sources were used to create distribution maps from 1957 to 2021. A predictive statistical model utilizing logistic regression was developed using significant environmental factors. Interpolation maps were created using the inverse distance weighted (IDW) method and overlaid with the corresponding environmental variables.
RESULTS: Based on the IDW analysis, high vector abundances were found in the southwestern part of Sarawak, the northern region of Pahang and the northwestern part of Sabah. However, most parts of Johor, Sabah, Perlis, Penang, Kelantan and Terengganu had low vector abundance. The accuracy test indicated that the model predicted sampling and non-sampling areas with 75.3% overall accuracy. The selected environmental variables were entered into the regression model based on their significant values. In addition to the presence of water bodies, elevation, temperature, forest loss and forest cover were included in the final model since these were significantly correlated. Anopheles mosquitoes were mainly distributed in Peninsular Malaysia (Titiwangsa range, central and northern parts), Sabah (Kudat, West Coast, Interior and Tawau division) and Sarawak (Kapit, Miri, and Limbang). The predicted Anopheles mosquito density was lower in the southern part of Peninsular Malaysia, the Sandakan Division of Sabah and the western region of Sarawak.
CONCLUSION: The study offers insight into the distribution of the Leucosphyrus Group of Anopheles mosquitoes in Malaysia. Additionally, the accompanying predictive vector map correlates well with cases of P. knowlesi malaria. This research is crucial in informing and supporting future efforts by healthcare professionals to develop effective malaria control interventions.