Dengue fever is a mosquito-borne disease that affects nearly 3.9 billion people globally. Dengue remains endemic in Malaysia since its outbreak in the 1980's, with its highest concentration of cases in the state of Selangor. Predictors of dengue fever outbreaks could provide timely information for health officials to implement preventative actions. In this study, five districts in Selangor, Malaysia, that demonstrated the highest incidence of dengue fever from 2013 to 2017 were evaluated for the best machine learning model to predict Dengue outbreaks. Climate variables such as temperature, wind speed, humidity and rainfall were used in each model. Based on results, the SVM (linear kernel) exhibited the best prediction performance (Accuracy = 70%, Sensitivity = 14%, Specificity = 95%, Precision = 56%). However, the sensitivity for SVM (linear) for the testing sample increased up to 63.54% compared to 14.4% for imbalanced data (original data). The week-of-the-year was the most important predictor in the SVM model. This study exemplifies that machine learning has respectable potential for the prediction of dengue outbreaks. Future research should consider boosting, or using, nature inspired algorithms to develop a dengue prediction model.
Three novel insect-specific flaviviruses, isolated from mosquitoes collected in Peru, Malaysia (Sarawak), and the United States, are characterized. The new viruses, designated La Tina, Kampung Karu, and Long Pine Key, respectively, are antigenically and phylogenetically more similar to the mosquito-borne flavivirus pathogens, than to the classical insect-specific viruses like cell fusing agent and Culex flavivirus. The potential implications of this relationship and the possible uses of these and other arbovirus-related insect-specific flaviviruses are reviewed.