Background In South Asia, hundreds of millions of people are infected with soil-transmitted helminths ( Ascaris lumbricoides , hookworm, and Trichuris trichiura ). However, high-resolution risk profiles and the estimated number of people infected have yet to be determined. In turn, such information will assist control programs to identify priority areas for allocation of scarce resource for the control of soil-transmitted helminth infection. Methodology We pursued a systematic review to identify prevalence surveys pertaining to soil-transmitted helminth infections in four mainland countries (i.e., Bangladesh, India, Nepal, and Pakistan) of South Asia. PubMed and ISI Web of Science were searched from inception to April 25, 2019, without restriction of language, study design, and survey date. We utilized Bayesian geostatistical models to identify environmental and socioeconomic predictors, and to estimate infection risk at high spatial resolution across the study region. Principal findings A total of 536, 490, and 410 georeferenced surveys were identified for A . lumbricoides , hookworm, and T . trichiura , respectively. We estimate that 361 million people (95% Bayesian credible interval (BCI) 331–395 million), approximately one-quarter of the South Asia population, was infected with at least one soil-transmitted helminth species in 2015. A . lumbricoides was the predominant species. Moderate to high prevalence (>20%) of any soil-transmitted helminth infection was predicted in the northeastern part and some northern areas of the study region, as well as the southern coastal areas of India. The annual treatment needs for the school-age population requiring preventive chemotherapy was estimated at 165 million doses (95% BCI: 146–185 million). Conclusions/significance Our risk maps provide an overview of the geographic distribution of soil-transmitted helminth infection in four mainland countries of South Asia and highlight the need for up-to-date surveys to accurately evaluate the disease burden in the region.

Background-The World Health Organization (WHO) has recently developed a predictive model to evaluate the impact of preventive chemotherapy programmes to control the morbidity of soil-transmitted helminths (STH). To make predictions, this model needs baseline information about proportion of infections classified as low, moderate and high intensity, for each of the three STH species, however the epidemiological data available are often limited to prevalence estimates.

Previous direct observations of the sediment surface in Vidy Bay, Lake Geneva (Switzerland), revealed a range of sediment characteristics in terms of colour, texture and morphology. Dives with the MIR submersibles during the éLEMO project permitted the exploration of a large portion of Vidy Bay. It is the most contaminated part of Lake Geneva, due to inputs of treated and untreated waters from a large wastewater treatment plant (WWTP). To evaluate the influence of WWTP effluent on mercury contamination and sediment characteristics, 14 sediment cores were retrieved in the vicinity of the wastewater treatment plant effluent. Total mercury concentrations in sediments ranged between 0.32 and 10.1 mg/kg. Inorganic mercury and monomethylmercury concentrations in overlying and pore waters were also measured. The total partition coefficients of mercury (logK d ) ranged from 3.6 to 5.8. The monomethylmercury concentration in pore waters of surface sediments was a large proportion of the total mercury concentration (44 ± 25 %). A Spearman test showed a negative correlation between the distance to the wastewater treatment plant outlet and the concentrations of total mercury in sediments and pore waters. Visual observations from the submersible allowed recognizing six different types of sediment. The areal distribution of these different sediment types clearly showed the influence of the wastewater treatment plant outlet on the sediment surface patterns. However, no relationship with mercury concentrations could be established.

BackgroundEstimating the reduction in levels of infection during implementation of soil-transmitted helminth (STH) control programmes is important to measure their performance and to plan interventions. Markov modelling techniques have been used with some success to predict changes in STH prevalence following treatment in Viet Nam. The model is stationary and to date, the prediction has been obtained by calculating the transition probabilities between the different classes of intensity following the first year of drug distribution and assuming that these remain constant in subsequent years. However, to run this model longitudinal parasitological data (including intensity of infection) are required for two consecutive years from at least 200 individuals. Since this amount of data is not often available from STH control programmes, the possible application of the model in control programme is limited. The present study aimed to address this issue by adapting the existing Markov model to allow its application when a more limited amount of data is available and to test the predictive capacities of these simplified models.MethodWe analysed data from field studies conducted with different combination of three parameters: (i) the frequency of drug administration; (ii) the drug distributed; and (iii) the target treatment population (entire population or school-aged children only). This analysis allowed us to define 10 sets of standard transition probabilities to be used to predict prevalence changes when only baseline data are available (simplified model 1). We also formulated three equations (one for each STH parasite) to calculate the predicted prevalence of the different classes of intensity from the total prevalence. These equations allowed us to design a simplified model (SM2) to obtain predictions when the classes of intensity at baseline were not known. To evaluate the performance of the simplified models, we collected data from the scientific literature on changes in STH prevalence during the implementation of 26 control programmes in 16 countries. Using the baseline data observed, we applied the simplified models and predicted the onward prevalence of STH infection at each time-point for which programme data were available. We then compared the output from the model with the observed data from the programme.ResultsThe comparison between the model-predicted prevalence and the observed values demonstrated a good accuracy of the predictions. In 77% of cases the original model predicted a prevalence within five absolute percentage points from the observed figure, for the simplified model one in 69% of cases and for the simplified model two in 60% of cases. We consider that the STH Markov model described here could be an important tool for programme managers to monitor the progress of their control programmes and to select the appropriate intervention. We also developed, and made freely available online, a software tool to enable the use of the STH Markov model by personnel with limited knowledge of mathematical models.