Biology of Dengue Vectors and Their Control in Thailand

The vectors of dengue, dengue haemorrhagic fever, and dengue shock syndrome are Aedes aegypti in the urban and rural areas and Aedes albopictus in the rural area. Aedes albolateralis a species member in Aedes niveus subgroup is highly susceptible to dengue 2 virus in laboratory. This species breeds in the forest in bamboo stump and tree hole and may be a source of sylvatic transmission. The anthropophilic, diurnal and domestic habit of Aedes aegypti in the increasing population of the world sustain aegypti population. In South East Asia aegypti is now invading albopictus the original species. Some evidences in biology morphotaxonomy, biochemistry indicate the plasticity of the species. The control strategy mainly for Aedes aegypti are (a) emergency control to interrupt transmission (b) larvicide (c) environmental management. Integrated control would be emphasized. Primary health care aims at extending health services to all the population and participation of each section of the community is essential and under the supervision of vector control professional, making use of the extensive research on vectors and their control

Meteorological, environmental remote sensing and neural network analysis of the epidemiology of malaria transmission in Thailand

In many malarious regions malaria transmission roughly coincides with rainy seasons, which provide for more abundant larval habitats. In addition to precipitation, other meteorological and environmental factors may also influence malaria transmission. These factors can be remotely sensed using earth observing environmental satellites and estimated with seasonal climate forecasts. The use of remote sensing usage as an early warning tool for malaria epidemics have been broadly studied in recent years, especially for Africa, where the majority of the world’s malaria occurs. Although the Greater Mekong Subregion (GMS), which includes Thailand and the surrounding countries, is an epicenter of multidrug resistant falciparum malaria, the meteorological and environmental factors affecting malaria transmissions in the GMS have not been examined in detail. In this study, the parasitological data used consisted of the monthly malaria epidemiology data at the provincial level compiled by the Thai Ministry of Public Health. Precipitation, temperature, relative humidity, and vegetation index obtained from both climate time series and satellite measurements were used as independent variables to model malaria. We used neural network methods, an artificial-intelligence technique, to model the dependency of malaria transmission on these variables. The average training accuracy of the neural network analysis for three provinces (Kanchanaburi, Mae Hong Son, and Tak) which are among the provinces most endemic for malaria, is 72.8% and the average testing accuracy is 62.9% based on the 1994-1999 data. A more complex neural network architecture resulted in higher training accuracy but also lower testing accuracy. Taking into account of the uncertainty regarding reported malaria cases, we divided the malaria cases into bands (classes) to compute training accuracy. Using the same neural network architecture on the 19 most endemic provinces for years 1994 to 2000, the mean training accuracy weighted by provincial malaria cases was 73%. Prediction of malaria cases for 2001 using neural networks trained for 1994-2000 gave a weighted accuracy of 53%. Because there was a significant decrease (31%) in the number of malaria cases in the 19 provinces from 2000 to 2001, the networks overestimated malaria transmissions. The decrease in transmission was not due to climatic or environmental changes. Thailand is a country with long borders. Migrant populations from the neighboring countries enlarge the human malaria reservoir because these populations have more limited access to health care. This issue also confounds the complexity of modeling malaria based on meteorological and environmental variables alone. In spite of the relatively low resolution of the data and the impact of migrant populations, we have uncovered a reasonably clear dependency of malaria on meteorological and environmental remote sensing variables. When other contextual determinants do not vary significantly, using neural network analysis along with remote sensing variables to predict malaria endemicity should be feasible

Studies on Brugian filariasis and its vectors in southern Thailand

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Interspecific and sexual shape variation in the filariasis vectors Mansonia dives and Ma. bonneae

International audienceIn the South of Thailand, six Mansonia species are recorded as filariasis vectors, among which Ma. bonneae and Ma. dives. These two species are distributed in the same breeding place, mainly the swamp forest, but appear to be of problematic identification using traditional morphological characters. Because of the risk of wrong identification during epidemiological or biological studies, complementary techniques are needed to distinguish the two species

НАРО́ДНОЕ СОБРА́НИЕ

FIGURE 1. Wings of female biting midges newly recorded from Thailand: 1. C. tamada, 2. C. kinari, 3. C. parabubalus, 4. C. spiculae, 5. C. flavipunctatus, 6. C. hui, 7. C. quatei, 8. C. arenicola, 9. C. kusaiensis and 10. C. pseudocordiger