Snail control to complement mass drug administration is being promoted by the World Health Organization for schistosomiasis control. Oncomelania hupensis quadrasi, the snail intermediate host of Schistosoma japonicum in the Philippines, has a very focal distribution; thus, scrutinizing baseline data and parameters affecting this distribution is very crucial. In this study in Gonzaga, Cagayan, Philippines, snail habitats were surveyed, and the various factors affecting the existence of the snails were determined. Malacological surveys and the mapping of sites of perpetual wetness in five endemic and five neighboring non-endemic barangays were conducted. Environmental and physicochemical factors were also examined. Maps of both snail and non-snail sites were generated. Of the fifty sites surveyed, O. h. quadrasi were found in twelve sites, and two sites yielded snails that were infected with S. japonicum cercariae. Factors such as silty loam soil, proximity to a snail site, water ammonia, and soil attributes (organic matter, iron, and pH) are all significantly associated with the presence of snails. In contrast, types of habitats, temperatures, and soil aggregation have no established association with the existence of snails. Mapping snail sites and determining factors favoring snail presence are vital to eliminating snails. These approaches will significantly maximize control impact and minimize wasted efforts and resources, especially in resource-limited schistosomiasis endemic areas.

Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant for the reliable identification of at-risk populations. Ecological fallacy occurs when ecological or group-level analyses, such as spatial aggregations at a specific administrative level, are carried out for an individual-level inference. This could lead to the unreliable identification of at-risk populations, and consequently to fallacies in the drugs’ allocation strategies and their cost-effectiveness. A specific form of ecological fallacy is pure specification bias. The present research aims to quantify its effect on the parameter estimates of various environmental covariates used as drivers for SCH infection. This is done by (i) using a spatial convolution model that removes pure specification bias, (ii) estimating group and individual-level covariate regression parameters, and (iii) quantifying the difference between the parameter estimates and the predicted disease outcomes from the convolution and ecological models. We modeled the prevalence of Schistosoma japonicum using group-level health outcome data, and city-level environmental data as a proxy for individual-level exposure. We included environmental data such as water and vegetation indexes, distance to water bodies, day and night land surface temperature, and elevation. We estimated and compared the convolution and ecological model parameter estimates using Bayesian statistics. Covariate parameter estimates from the convolution and ecological models differed between 0.03 for the nearest distance to water bodies (NDWB), and 0.28 for the normalized difference water index (NDWI). The convolution model presented lower uncertainties in most of the parameter estimates, except for NDWB. High differences in uncertainty were found in night land surface temperature (0.23) and elevation (0.13). No significant differences were found between the predicted values and their uncertainties from both models. The proposed convolution model is able to correct for a pure specification bias by presenting less uncertain parameter estimates. It shows a good predictive performance for the mean prevalence values and for a positive number of infected people. Further research is needed to better understand the spatial extent and support of analysis to reliably explore the role of environmental variables.

Oncomelania hupensis quadrasi is the intermediate host of S. japonicum, the causative species of schistosomiasis in the Philippines. Conventionally, risk areas are identified by procedures requiring highly skilled personnel and constant surveillance efforts. Recent developments in disease diagnostics explore the utilization of environmental DNA as targets for polymerase chain reactions in disease surveillance. In this study, a low-cost, specific, and efficient SYBR Green-based real-time PCR assay to detect O. h. quadrasi DNA from water samples was developed, optimized, and validated. Primers were designed based on the A18 microsatellite region of O. h. quadrasi. The assay exhibited a detection limit of one copy number per microliter at 99.4% efficiency and R² = 0.999, which specifically amplified O. h. quadrasi DNA only. Validation of this assay in environmental water samples demonstrated 100% PPV and NPV values, suggesting its potential as a tool for identifying risk areas, pathogen-directed surveillance, policy making, and disease control.