Climatic and social risk factors for Aedes infestation in rural Thailand.

An intense epidemic of dengue haemorrhagic fever in 1998 prompted the Thai government to investigate the feasibility of focalized vector (Aedes aegypti) control programmes. We tested for correlations of three indices of Aedes larval abundance (housing index, container index and Breteau index) against 38 socio-economic and four climatic variables. Availability of public water wells, existence of transport services and proportion of tin houses were positively associated with larval indices. Private water wells, health education, health insurance coverage, thatched houses and use of firewood for cooking were negatively associated. These probably represent both direct effects on breeding sites (private vs. public wells decrease necessity to store water, and health education may encourage breeding site removal), and more general effects of health-related attitudes, housing quality and remoteness from urban areas. Indices were positively associated with daily minimum temperature, an increase in precipitation from the previous month (reflecting the onset of the rainy season) and daily maximum temperatures of approximately 33-34 degrees C. The associations were used to derive statistical models to predict the rank order of larval indices within the study area (Spearman's correlation coefficients = 0.525-0.554). The study provides a rational basis for identifying possible social interventions, and for prioritizing previously unsurveyed villages for further monitoring and focalized vector control

Combined sterile insect technique and incompatible insect technique: The first proof-of-concept to suppress Aedes aegypti vector populations in semi-rural settings in Thailand.

BACKGROUND:Important arboviral diseases, such as dengue, chikungunya, and Zika virus infections, are transmitted mainly by the Aedes aegypti vector. So far, controlling this vector species with current tools and strategies has not demonstrated sustainable and significant impacts. Our main objective was to evaluate whether open field release of sterile males, produced from combining the sterile insect technique using radiation with the insect incompatible technique through Wolbachia-induced incompatibility (SIT/IIT), could suppress natural populations of Ae. aegypti in semi-rural village settings in Thailand. METHODOLOGY/PRINCIPAL FINDINGS:Irradiated Wolbachia-infected Aedes aegypti males produced by the SIT/IIT approach were completely sterile and were able to compete with the wild fertile ones. Open field release of these sterile males was conducted in an ecologically isolated village in Chachoengsao Province, eastern Thailand. House-to-house visit and media reports resulted in community acceptance and public awareness of the technology. During intervention, approximately 100-200 sterile males were released weekly in each household. After 6 months of sterile male release, a significant reduction (p<0.05) of the mean egg hatch rate (84%) and the mean number of females per household (97.30%) was achieved in the treatment areas when compared to the control ones. CONCLUSIONS/SIGNIFICANCE:Our study represents the first open field release of sterile Ae. aegypti males developed from a combined SIT/IIT approach. Entomological assessment using ovitraps, adult sticky traps, and portable vacuum aspirators confirmed the success in reducing natural populations of Ae. aegypti females in treated areas. Public awareness through media resulted in positive support for practical use of this strategy in wider areas. Further study using a systematic randomized trial is needed to determine whether this approach could have a significant impact on the diseases transmitted by Ae. aegypti vector

Predictiveness of Disease Risk in a Global Outreach Tourist Setting in Thailand Using Meteorological Data and Vector-Borne Disease Incidences

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate

Population characteristics of <i>Aedes (Stegomyia) aegypti</i> (L.) sampled from five villages in Chachoengsao Province, eastern Thailand (<i>r</i> = allelic richness, <i>F</i>_IS = inbreeding coefficient, <i>H</i>_E = expected heterozygosity, <i>H</i>_O = observed heterozygosity, HW-<i>P</i> = Hardy Weinberg <i>P</i> value, *significant after correction for multiple comparisons using False Discovery Rate procedures [12], N/S = no spatial autocorrelation at distances tested), Relatedness estimator [22], GGD = geographic distance.

<p>Population characteristics of <i>Aedes (Stegomyia) aegypti</i> (L.) sampled from five villages in Chachoengsao Province, eastern Thailand (<i>r</i> = allelic richness, <i>F</i>_IS = inbreeding coefficient, <i>H</i>_E = expected heterozygosity, <i>H</i>_O = observed heterozygosity, HW-<i>P</i> = Hardy Weinberg <i>P</i> value, *significant after correction for multiple comparisons using False Discovery Rate procedures <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001913#pntd.0001913-Goudet1" target="_blank">[12]</a>, N/S = no spatial autocorrelation at distances tested), Relatedness estimator <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001913#pntd.0001913-Waples2" target="_blank">[22]</a>, GGD = geographic distance.</p

Pairwise population comparisons of <i>Aedes (Stegomyia) aegypti</i> (L.) from five villages in Chachoengsao Province, eastern Thailand.

<p>Upper diagonal shows pairwise <i>F</i>_ST estimates, lower diagonal shows pairwise geographic distance (km). Significance of <i>F</i>_ST estimates (obtained by 15300 permutations) at the indicative adjusted nominal level (5%) of <i>P</i><0.000327 is indicated by bold type.</p

Estimates of effective population size (<i>N</i>_e) of <i>Aedes (Stegomyia) aegypti</i> (L.) in five villages in Thailand based on Waples' [20] method using temporal differences in allele frequency across 9 generations.

<p>Estimates of effective population size (<i>N</i>_e) of <i>Aedes (Stegomyia) aegypti</i> (L.) in five villages in Thailand based on Waples' <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0001913#pntd.0001913-Waples1" target="_blank">[20]</a> method using temporal differences in allele frequency across 9 generations.</p

Complex of five villages in Hua Sam Rong Subdistrict of Plaeng Yao District, Chachoengsao Province, eastern Thailand, from which samples of <i>Aedes (Stegomyia) aegypti</i> (L.) were taken from December 2007 to September 2008.

<p>Complex of five villages in Hua Sam Rong Subdistrict of Plaeng Yao District, Chachoengsao Province, eastern Thailand, from which samples of <i>Aedes (Stegomyia) aegypti</i> (L.) were taken from December 2007 to September 2008.</p

Temporal differences in population structure in <i>Aedes (Stegomyia) aegypti</i> (L.) in a complex of five eastern Thai villages, identified with Factorial Correspondence Analysis ((A) Village 1; (B) Village 2; (C) Village 3; (D) Village 6 and (E) Village 11).

<p>Temporal differences in population structure in <i>Aedes (Stegomyia) aegypti</i> (L.) in a complex of five eastern Thai villages, identified with Factorial Correspondence Analysis ((A) Village 1; (B) Village 2; (C) Village 3; (D) Village 6 and (E) Village 11).</p