Update on the Geographic Distribution of the Intermediate Host Snails of Schistosoma mansoni on St. Lucia: A Step Toward Confirming the Interruption of Transmission of Human Schistosomiasis.

To provide information to guide considerations of declaring interruption of transmission of human schistosomiasis due to Schistosoma mansoni on St. Lucia, we undertook an island-wide survey in June-July 2022 to determine the presence of Biomphalaria snails, the intermediate hosts of S. mansoni, and their infection status. Snail surveys were carried out at 58 habitats to determine presence of Biomphalaria snails followed by examination of the collected snails for evidence of infection with S. mansoni. Furthermore, water samples were collected at the snail habitats and screened for presence of S. mansoni DNA using an eDNA approach. We found B. glabrata present in one habitat (Cul de Sac) where it was abundant. Specimens provisionally identified as Biomphalaria kuhniana were recovered from 10 habitats. None of the Biomphalaria specimens recovered were positive for S. mansoni. None of the eDNA water samples screened were positive for S. mansoni. Experimental exposures of both field-derived and laboratory-reared St. Lucian B. glabrata and B. kuhniana to Puerto Rican and Kenyan-derived S. mansoni strains revealed B. glabrata to be susceptible to both and B. kuhniana proved refractory from histological and snail shedding results. We conclude, given the current rarity of B. glabrata on the island and lack of evidence for the presence of S. mansoni, that transmission is unlikely to be ongoing. Coupled with negative results from recent human serological surveys, and implementation of improved sanitation and provision of safe water supplies, St. Lucia should be considered a candidate for declaration of interruption of human schistosomiasis transmission.

Lessons from the Field: Integrated survey methodologies for neglected tropical diseases.

The 2021-2030 Neglected Tropical Diseases road map calls for intensified cross-cutting approaches. By moving away from vertical programming, the integration of platforms and intervention delivery aims to improve efficiency, cost-effectiveness and programme coverage. Drawing on the direct experiences of the authors, this article outlines key elements for successful integrated surveys, the challenges encountered, as well as future opportunities and threats to such surveys. There are multiple advantages. Careful planning should ensure that integration does not result in a process that is less efficient, more expensive or that generates data driving less reliable decisions than conducting multiple disease-specific surveys.

Evaluation of a linear spectral mixture model and vegetation indices (NDVI and EVI) in a study of schistosomiasis mansoni and Biomphalaria glabrata distribution in the state of Minas Gerais, Brazil.

This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism.

Geographical Information System (GIS) is a tool that has recently been applied to better understand spatial disease distributions. Using meteorological, social, sanitation, mollusc distribution data and remote sensing variables, this study aimed to further develop the GIS technology by creating a model for the spatial distribution of schistosomiasis and to apply this model to an area with rural tourism in the Brazilian state of Minas Gerais (MG). The Estrada Real, covering about 1,400 km, is the largest and most important Brazilian tourism project, involving 163 cities in MG with different schistosomiasis prevalence rates. The model with three variables showed a R(2) = 0.34, with a standard deviation of risk estimated adequate for public health needs. The main variables selected for modelling were summer vegetation, summer minimal temperature and winter minimal temperature. The results confirmed the importance of Remote Sensing data and the valuable contribution of GIS in identifying priority areas for intervention in tourism regions which are endemic to schistosomiasis.

Predictive risk mapping of schistosomiasis in Brazil using Bayesian geostatistical models.

Schistosomiasis is one of the most common parasitic diseases in tropical and subtropical areas, including Brazil. A national control programme was initiated in Brazil in the mid-1970s and proved successful in terms of morbidity control, as the number of cases with hepato-splenic involvement was reduced significantly. To consolidate control and move towards elimination, there is a need for reliable maps on the spatial distribution of schistosomiasis, so that interventions can target communities at highest risk. The purpose of this study was to map the distribution of Schistosoma mansoni in Brazil. We utilized readily available prevalence data from the national schistosomiasis control programme for the years 2005-2009, derived remotely sensed climatic and environmental data and obtained socioeconomic data from various sources. Data were collated into a geographical information system and Bayesian geostatistical models were developed. Model-based maps identified important risk factors related to the transmission of S. mansoni and confirmed that environmental variables are closely associated with indices of poverty. Our smoothed predictive risk map, including uncertainty, highlights priority areas for intervention, namely the northern parts of North and Southeast regions and the eastern part of Northeast region. Our predictive risk map provides a useful tool for to strengthen existing surveillance-response mechanisms.

Bayesian geostatistical modeling of leishmaniasis incidence in Brazil.

BACKGROUND: Leishmaniasis is endemic in 98 countries with an estimated 350 million people at risk and approximately 2 million cases annually. Brazil is one of the most severely affected countries. METHODOLOGY: We applied Bayesian geostatistical negative binomial models to analyze reported incidence data of cutaneous and visceral leishmaniasis in Brazil covering a 10-year period (2001-2010). Particular emphasis was placed on spatial and temporal patterns. The models were fitted using integrated nested Laplace approximations to perform fast approximate Bayesian inference. Bayesian variable selection was employed to determine the most important climatic, environmental, and socioeconomic predictors of cutaneous and visceral leishmaniasis. PRINCIPAL FINDINGS: For both types of leishmaniasis, precipitation and socioeconomic proxies were identified as important risk factors. The predicted number of cases in 2010 were 30,189 (standard deviation [SD]: 7,676) for cutaneous leishmaniasis and 4,889 (SD: 288) for visceral leishmaniasis. Our risk maps predicted the highest numbers of infected people in the states of Minas Gerais and Pará for visceral and cutaneous leishmaniasis, respectively. CONCLUSIONS/SIGNIFICANCE: Our spatially explicit, high-resolution incidence maps identified priority areas where leishmaniasis control efforts should be targeted with the ultimate goal to reduce disease incidence.

Soil-transmitted helminth infection in South America: a systematic review and geostatistical meta-analysis.

BACKGROUND: The four common soil-transmitted helminth species-Ascaris lumbricoides, Trichuris trichiura, and the two hookworm species Ancylostoma duodenale and Necator americanus-are endemic in South America, but their distribution, infection prevalence, and regional burden are poorly understood. We aimed to estimate the risk and number of people infected with A lumbricoides, T trichiura, and hookworm across South America. METHODS: We did a systematic review of reports on the prevalence of soil-transmitted helminth infection in South America published up to May 14, 2012. We extracted and georeferenced relevant survey data and did a meta-analysis of the data to assess the geographical distribution of the infection risk with Bayesian geostatistical models. We used advanced Bayesian variable selection to identify environmental determinants that govern the distribution of soil-transmitted helminth infections. FINDINGS: We screened 4085 scientific papers and identified 174 articles containing relevant survey prevalence data. We georeferenced 6948 survey locations and entered the data into the open-access Global Neglected Tropical Diseases database. Survey data were sparse for the south of the continent and for the western coast, and we identified no relevant information for Uruguay and little data for smaller countries such as Suriname, Guyana, French Guiana, and Ecuador. Population-adjusted prevalence of infection with A lumbricoides was 15·6%, with T trichiura was 12·5%, and with hookworm was 11·9% from 2005 onwards. Risks of contracting soil-transmitted helminth infection have substantially reduced since 2005 (odds ratio 0·47 [95% Bayesian credible interval 0·46-0·47] for A lumbricoides, 0·54 [0·54-0·55] for T trichiura, and 0·58 [0·58-0·59] for hookworm infection). INTERPRETATION: Our findings offer important baseline support for spatial targeting of soil-transmitted helminthiasis control, and suggest that more information about the prevalence of soil-transmitted helminth infection is needed, especially in countries in which we estimate prevalence of infection to be high but for which current data are scarce. FUNDING: UBS Optimus Foundation and Brazilian Swiss Joint Research Programme (BSJRP 011008).

Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models.

Soil-transmitted helminths (Ascaris lumbricoides, Trichuris trichiura and hookworm) negatively impact the health and wellbeing of hundreds of millions of people, particularly in tropical and subtropical countries, including Brazil. Reliable maps of the spatial distribution and estimates of the number of infected people are required for the control and eventual elimination of soil-transmitted helminthiasis. We used advanced Bayesian geostatistical modelling, coupled with geographical information systems and remote sensing to visualize the distribution of the three soil-transmitted helminth species in Brazil. Remotely sensed climatic and environmental data, along with socioeconomic variables from readily available databases were employed as predictors. Our models provided mean prevalence estimates for A. lumbricoides, T. trichiura and hookworm of 15.6%, 10.1% and 2.5%, respectively. By considering infection risk and population numbers at the unit of the municipality, we estimate that 29.7 million Brazilians are infected with A. lumbricoides, 19.2 million with T. trichiura and 4.7 million with hookworm. Our model-based maps identified important risk factors related to the transmission of soiltransmitted helminths and confirm that environmental variables are closely associated with indices of poverty. Our smoothed risk maps, including uncertainty, highlight areas where soil-transmitted helminthiasis control interventions are most urgently required, namely in the North and along most of the coastal areas of Brazil. We believe that our predictive risk maps are useful for disease control managers for prioritising control interventions and for providing a tool for more efficient surveillance-response mechanisms.

Modelling the geographical distribution of soil-transmitted helminth infections in Bolivia.

BACKGROUND: The prevalence of infection with the three common soil-transmitted helminths (i.e. Ascaris lumbricoides, Trichuris trichiura, and hookworm) in Bolivia is among the highest in Latin America. However, the spatial distribution and burden of soil-transmitted helminthiasis are poorly documented. METHODS: We analysed historical survey data using Bayesian geostatistical models to identify determinants of the distribution of soil-transmitted helminth infections, predict the geographical distribution of infection risk, and assess treatment needs and costs in the frame of preventive chemotherapy. Rigorous geostatistical variable selection identified the most important predictors of A. lumbricoides, T. trichiura, and hookworm transmission. RESULTS: Results show that precipitation during the wettest quarter above 400 mm favours the distribution of A. lumbricoides. Altitude has a negative effect on T. trichiura. Hookworm is sensitive to temperature during the coldest month. We estimate that 38.0%, 19.3%, and 11.4% of the Bolivian population is infected with A. lumbricoides, T. trichiura, and hookworm, respectively. Assuming independence of the three infections, 48.4% of the population is infected with any soil-transmitted helminth. Empirical-based estimates, according to treatment recommendations by the World Health Organization, suggest a total of 2.9 million annualised treatments for the control of soil-transmitted helminthiasis in Bolivia. CONCLUSIONS: We provide estimates of soil-transmitted helminth infections in Bolivia based on high-resolution spatial prediction and an innovative variable selection approach. However, the scarcity of the data suggests that a national survey is required for more accurate mapping that will govern spatial targeting of soil-transmitted helminthiasis control.

Spatial distribution of Biomphalaria spp., the intermediate host snails of Schistosoma mansoni, in Brazil.

Schistosomiasis mansoni remains an important parasitic disease of man, endemic in large parts of sub-Saharan Africa, the Middle East, South America and the Caribbean. The aetiological agent is the trematode Schistosoma mansoni, whereas aquatic snails of the genus Biomphalaria act as intermediate hosts in the parasite life cycle. In Brazil, the distribution of Biomphalaria spp. is closely associated with the occurrence of schistosomiasis. The purpose of this study was to map and predict the spatial distribution of the intermediate host snails of S. mansoni across Brazil. We assembled snail "presenceonly" data and used a maximum entropy approach, along with climatic and environmental variables to produce predictive risk maps. We identified a series of risk factors that govern the distribution of Biomphalaria snails. We find that high-risk areas for B. glabrata are concentrated in the regions of Northeast and Southeast and the northern part of the South region. B. straminea are found in the Northeast and Southeast regions, and B. tenagophila are concentrated in the Southeast and South regions. Our findings confirm that the presence of the intermediate host snails is correlated with the occurrence of schistosomiasis mansoni. The generated risk maps of intermediate host snails might assist the national control programme for spatial targeting of control interventions and to ultimately move towards schistosomiasis elimination in Brazil.

Environmental and socio-economic risk modelling for Chagas disease in Bolivia.

Accurately defining disease distributions and calculating disease risk is an important step in the control and prevention of diseases. Geographical information systems (GIS) and remote sensing technologies, with maximum entropy (Maxent) ecological niche modelling computer software, were used to create predictive risk maps for Chagas disease in Bolivia. Prevalence rates were calculated from 2007 to 2009 household infection survey data for Bolivia, while environmental data were compiled from the Worldclim database and MODIS satellite imagery. Socio-economic data were obtained from the Bolivian National Institute of Statistics. Disease models identified altitudes at 500-3,500 m above the mean sea level (MSL), low annual precipitation (45-250 mm), and higher diurnal range of temperature (10-19 °C; peak 16 °C) as compatible with the biological requirements of the insect vectors. Socio-economic analyses demonstrated the importance of improved housing materials and water source. Home adobe wall materials and having to fetch drinking water from rivers or wells without pump were found to be highly related to distribution of the disease by the receiver operator characteristic (ROC) area under the curve (AUC) (0.69 AUC, 0.67 AUC and 0.62 AUC, respectively), while areas with hardwood floors demonstrated a direct negative relationship (-0.71 AUC). This study demonstrates that Maxent modelling can be used in disease prevalence and incidence studies to provide governmental agencies with an easily learned, understandable method to define areas as either high, moderate or low risk for the disease. This information may be used in resource planning, targeting and implementation. However, access to high-resolution, sub-municipality socio-economic data (e.g. census tracts) would facilitate elucidation of the relative influence of poverty-related factors on regional disease dynamics.

Utilizing environmental, socioeconomic data and GIS techniques to estimate the risk for ascariasis and trichuriasis in Minas Gerais, Brazil.

The impact of intestinal helminths on human health is well known among the population and health authorities because of their wide geographic distribution and the serious problems they cause. Geohelminths are highly prevalent and have a big impact on public health, mainly in underdeveloped and developing countries. Geohelminths are responsible for the high levels of debility found in the younger population and are often related to cases of chronic diarrhea and malnutrition, which put the physical and intellectual development of children at risk. These geohelminths have not been sufficiently studied. One obstacle in implementing a control program is the lack of knowledge of the prevalence and geographical distribution. Geographical information systems (GIS) and remote sensing (RS) have been utilized to improve understanding of infectious disease distribution and climatic patterns. In this study, GIS and RS technologies, as well as meteorological, social, and environmental variables were utilized for the modeling and prediction of ascariasis and trichuriasis. The GIS and RS technologies specifically used were those produced by orbital sensing including the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). The results of this study demonstrated important factors related to the transmission of ascariasis and trichuriasis and confirmed the key association between environmental variables and the poverty index, which enabled us to identify priority areas for intervention planning in the state of Minas Gerais in Brazil.

Spatial distribution of Biomphalaria mollusks at São Francisco River Basin, Minas Gerais, Brazil, using geostatistical procedures.

Geostatistics is used in this work to make inferences about the presence of the species of Biomphalaria (B. glabrata, B. tenagophila and/or B. straminea), intermediate hosts of Schistosoma mansoni, at the São Francisco River Basin, in Minas Gerais, Brazil. One of these geostatistical procedures, known as indicator kriging, allows the classification of categorical data, in areas where the data are not available, using a punctual sample set. The result is a map of species and risk area definition. More than a single map of the categorical attribute, the procedure also permits the association of uncertainties of the stochastic model, which can be used to qualify the inferences. In order to validate the estimated data of the risk map, a fieldwork in five municipalities was carried out. The obtained results showed that indicator kriging is a rather robust tool since it presented a very good agreement with the field findings. The obtained risk map can be thought as an auxiliary tool to formulate proper public health strategies, and to guide other fieldwork, considering the places with higher occurrence probability of the most important snail species. Also, the risk map will enable better resource distribution and adequate policies for the mollusk control. This methodology will be applied to other river basins to generate a predictive map for Biomphalaria species distribution for the entire state of Minas Gerais.

Schistosomiasis risk estimation in Minas Gerais State, Brazil, using environmental data and GIS techniques.

The influence of climate and environmental variables to the distribution of schistosomiasis has been assessed in several previous studies. Also Geographical Information System (GIS), is a tool that has been recently tested for better understanding the spatial disease distribution. The objective of this paper is to further develop the GIS technology for modeling and control of schistosomiasis using meteorological and social variables and introducing new potential environmental-related variables, particularly those produced by recently launched orbital sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Shuttle Radar Topography Mission (SRTM). Three different scenarios have been analyzed, and despite of not quite large determination factor, the standard deviation of risk estimates was considered adequate for public health needs. The main variables selected as important for modeling purposes was topographic elevation, summer minimum temperature, the NDVI vegetation index, and the social index HDI91.

Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data.

The aim of this work is to establish a relationship between schistosomiasis prevalence and social-environmental variables, in the state of Minas Gerais, Brazil, through multiple linear regression. The final regression model was established, after a variables selection phase, with a set of spatial variables which contains the summer minimum temperature, human development index, and vegetation type variables. Based on this model, a schistosomiasis risk map was built for Minas Gerais.

Analysis and estimative of schistosomiasis prevalence for the state of Minas Gerais, Brazil, using multiple regression with social and environmental spatial data

The aim of this work is to establish a relationship between schistosomiasis prevalence and social-environmental variables, in the state of Minas Gerais, Brazil, through multiple linear regression. The final regression model was established, after a variables selection phase, with a set of spatial variables which contains the summer minimum temperature, human development index, and vegetation type variables. Based on this model, a schistosomiasis risk map was built for Minas Gerais.