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Transmissibility is a significant factor in parasite fitness. The rate and magnitude of parasite transmission affect prevalence and infection intensity in individual hosts and are influenced by environmental factors. In this context, the objectives of this study were: (i) to experimentally assess Opisthorchis viverrini miracidia survival and infectivity over time and across temperatures; and (ii) to combine these experimental results with environmental data to build a key component of a transmission model, identifying seasonal windows of transmission risk in hyper-endemic northeastern Thailand. Five replicates of 50 O. viverrini eggs were randomly distributed and maintained under four temperature conditions (25°C, 30°C, 35°C, 40°C). Microscopic observations were performed on all experimental units over a period of 3 months to record miracidia motility and mortality trends. Six infection trials were also conducted to assess infectivity of miracidia over time and across temperatures, using observations of egg hatching success and infection rates. Upon completion of experiments, data were integrated into a transmission model to create a Transmission Risk Index and to simulate seasonal transmission risk. Miracidia survival rate and motility decreased steadily with 50% mortality observed after 2 weeks. Hatching and infection success also decreased significantly after 3 weeks. Temperatures over 30°C were associated with increased mortality and decreased infectivity. When incorporating local environmental parameters into our model, we observed low transmission risk during the dry season and increasing transmission risk at the onset of the rainy season, culminating with the highest risk in September. We believe that our results provide the first estimates of O.viverrini miracidia survival and transmission potential under variable temperature conditions and suggest that high temperature treatment (> 40°C) of fecal waste could be an efficient control strategy.

Land use regression (LUR) models are widely used in epidemiological studies to assess exposure to air pollution. However, most of the existing LUR studies focus on estimating annual or monthly average concentration of air pollutants, with high spatial but low temporal resolution. In this paper, we combined LUR models with meteorological conditions to estimate daily nitrogen dioxide (NO2) and particulate matter (PM10) concentrations in the urban area of Changsha, China. Seventy-four sites for NO2 and thirty-six sites for PM10 were selected to build LUR models. The LUR models explained 51% and 62% of spatial variability for NO2 and PM10. The most important spatial explanatory variables included major roads, residential land and public facilities land, indicating that the spatial distributions of NO2 and PM10 are closely related to traffic conditions and human activities. Meteorological factors were introduced to model the temporal variability of NO2 and PM10 by using meteorological factors regression (MFR) and back propagation neural network (BPNN) modeling techniques. Important temporal explanatory variables included temperature, wind speed, cloud cover and percentage of haze. Pearson's r values between predicted and measured concentrations were much higher in BPNN models than in MFR models. The results demonstrate that the BPNN models showed a better performance than the MFR models in modeling temporal variation of NO2 and PM10. The approach of modeling spatial and temporal variation of air pollutants using LUR models coupled with meteorological conditions has potential usefulness for exposure assessment, especially for medium or short term exposure, in health studies.

Land use regression (LUR) models have proven to be a robust technique for predicting spatial distribution of pollutants with high resolution. Wind direction is an important factor affecting atmospheric environment quality. However, conventional LUR models have difficulties taking wind direction into consideration. This study put forward a semicircular-buffer-based (SCBB) LUR model to overcome this challenge. To assess the impact of wind direction on model performance, we set up two different LUR models for nitrogen dioxide (NO2) and particulate matter (PM10) in the urban area of Changsha, China. A location-allocation approach was used to identify sampling sites. Integrated 14-day mean concentrations of NO2 and PM10 were measured at 80 sites and 40 sites, respectively. Measured mean concentrations ranged from 17.0 to 75.7 for NO2 and 34.7 to 118.7 μg/m3 for PM10. Random samples of 75% of monitoring sites were used to the develop model and the remaining 25% of sites were retained for evaluation. Predictor variables were created in a geographic information system (GIS) and LUR models were developed with the most significant variables. The results showed SCBB LUR models had significantly higher R2 values than traditional LUR models, supporting the feasibility of this new approach incorporating wind direction in the LUR model.