Jakkrawarn Chompoosri

Insecticide Resistance in Bedbugs in Thailand and Laboratory Evaluation of Insecticides for the Control of Cimex hemipterus and Cimex lectularius (Hemiptera: Cimicidae)

ABSTRACT Bedbugs are found in many countries around the world, and in some regions they are resistant to numerous insecticides. This study surveyed bedbugs in Thailand and determined their resistance to insecticides. The surveys were carried out in six provinces that attract large numbers of foreign tourists: Bangkok, Chonburi, Chiang Mai, Ubon Ratchathani, Phuket, and Krabi. Bedbugs were collected from hotels and colonized in the laboratory to evaluate their resistance to insecticides. Cimex hemipterus (F.) was found in some hotels in Bangkok, Chonburi, Phuket, and Krabi, whereas Cimex lectularius L. was found only in hotels in Chiang Mai. No bedbugs were found in Ubon Ratchathani. The colonized bedbugs showed resistance to groups of insecticides, including organochlorines (dichlorodiphenyl trichloroethane, dieldrin), carbamates (bendiocarb, propoxur), organophosphates (malathion, fenitrothion), and pyrethroids (cyfluthrin, deltamethrin, permethrin, &lgr;-cyhalothrin, etofenprox) in tests using World Health Organization insecticide-impregnated papers. The new insecticides imidacloprid (neonicotinoid group), chlorfenapyr (pyrrole group), and fipronil (phenylpyrazole group) were effective against the bedbugs; however, organophosphate (diazinon), carbamates (fenobucarb, propoxur), and pyrethroids (bifenthrin, cypermethrin, esfenvalerate, etofenprox) were ineffective. Aerosols containing various pyrethroid insecticides with two to four different active ingredients were effective against the bedbugs. The results obtained from this study suggested that both species of bedbugs in Thailand have developed marked resistance to various groups of insecticides, especially those in the pyrethroid group, which are the most common insecticides used for pest control. Therefore, an integrated pest management should be implemented for managing bedbugs in Thailand.

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas.

Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE).