Spatio-ecological determinants of freshwater snail intermediate hosts and schistosome infection in the Lango subregion, Northern Uganda: a geostatistical approach to targeted disease control

Spatio-ecological determinants of freshwater snail intermediate hosts and schistosome infection in the Lango subregion, Northern Uganda: a geostatistical approach to targeted disease control | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatio-ecological determinants of freshwater snail intermediate hosts and schistosome infection in the Lango subregion, Northern Uganda: a geostatistical approach to targeted disease control John Paul Byagamy, Robert Opiro, Margaret Nyafwono, Harriet Angwech, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8643236/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Freshwater snails of the genera Biomphalaria , Bulinus , and Lymnaea serve as intermediate hosts for trematodes causing schistosomiasis and fascioliasis, diseases of major public health concern in sub-Saharan Africa. In Uganda's Lango subregion, schistosomiasis remains endemic despite control efforts, yet comprehensive spatial and ecological analyses of snail intermediate host distributions are lacking. This study employed geostatistical approaches to identify high-risk snail habitats and sites where schistosome cercarial shedding was detected to inform targeted control strategies. Methods A cross-sectional study was conducted during the dry and rainy seasons of 2023 across 26 georeferenced sites in Lira and Kole districts. Freshwater snails were collected using standardized methods and identified morphologically. Cercarial shedding tests determined infection status but were limited to detecting patent infections. Physicochemical parameters (pH, salinity, total dissolved solids, dissolved oxygen, temperature, and conductivity) and ecological variables were measured. Spatial analysis included Moran’s I for autocorrelation, Getis-Ord Gi* for hotspot detection, and inverse distance weighting for interpolation. Generalized linear mixed models with spatial random effects were used to assess predictors of snail prevalence and density, compared with non-spatial models using the Akaike Information Criterion. Results A total of 4,802 snails from 13 species were collected, with Biomphalaria choanomphala (25.8%) being most abundant. Significant spatial clustering was detected for B. choanomphala (Moran’s I = 0.32, p = 0.004) and B. sudanica (Moran’s I = 0.24, p = 0.018). Three density hotspots and one site where infected snails clustered were identified, primarily in rice paddies and swamps near human settlements. Overall infection rate was 0.15% (5/3404 tested snails), with B. choanomphala showing the highest infection 0.06% (2/1241). Spatial GLMMs outperformed non-spatial models (ΔAIC = 12.7–15.3), revealing significant effects of salinity (odds ratio = 0.21, p < 0.001), total dissolved solids (β = -0.03, p = 0.002), dissolved oxygen (β = 0.54, p = 0.003), and anthropogenic activities. Spatial random effects accounted for 18–24% of residual variation. Conclusions This study demonstrates the significant added value of geostatistical methods in identifying snail intermediate host clusters and sites with detected infections. The integration of spatial analysis with ecological modeling provides a robust framework for potential targeted snail control. Our findings suggest that focused interventions in identified high-density areas, integration of spatial risk maps into district health planning, and community engagement in modifying high-risk water contact sites could help reduce schistosomiasis transmission in the Lango subregion. Biomphalaria Bulinus Freshwater snails Geostatistics Hotspot mapping Lango subregion Schistosomiasis Spatial analysis Uganda Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Freshwater snails of the class Gastropoda play crucial ecological roles in aquatic ecosystems but also serve as intermediate hosts for medically important trematodes, particularly those causing schistosomiasis and fascioliasis [ 1 ]. Globally, schistosomiasis affects approximately 250 million people, with over 90% of cases occurring in sub-Saharan Africa [ 2 , 3 ]. In Uganda, schistosomiasis is endemic in approximately 81 districts, with an estimated 5.4 million people infected and 14.7 million at risk [ 4 , 5 ]. The disease burden is particularly high among school-aged children in endemic areas, leading to anemia, impaired growth, and reduced cognitive development [ 6 , 7 ]. The transmission dynamics of snail-borne diseases are closely connected to the distribution and abundance of specific freshwater snail intermediate hosts. Biomphalaria species transmit Schistosoma mansoni (intestinal schistosomiasis), Bulinus species transmit S. haematobium (urogenital schistosomiasis), and Lymnaea species transmit Fasciola spp. (fascioliasis) [ 8 , 9 ]. These snails thrive in various freshwater habitats including streams, ponds, swamps, and irrigation schemes, with their distribution affected by complex interactions between physicochemical factors, ecological conditions, and human activities [ 10 , 11 ]. Previous studies in Uganda have mainly focused on the Lake Victoria basin, where high snail densities and infection rates have been documented [ 12 – 14 ]. However, northern Uganda features distinct ecological and climatic conditions, characterized by seasonal rainfall patterns, extensive wetland systems, and different land use practices [ 15 , 16 ]. The Lango subregion, with about 30% of its area covered by swamps, rivers, and wetlands, presents a unique transmission setting that remains understudied [ 17 , 18 ]. Recent urbanization and agricultural expansion, especially rice cultivation, have altered aquatic habitats, potentially creating favorable conditions for snail proliferation [ 19 , 20 ]. Ecological studies have identified various factors influencing snail distributions, including water temperature, pH, dissolved oxygen, conductivity, vegetation cover, and substrate type [ 21 – 23 ]. However, these factors often exhibit spatial heterogeneity, creating patchy distributions of snail populations and disease transmission risk [ 24 , 25 ]. Traditional regression approaches that ignore spatial autocorrelation may produce biased estimates and inadequate predictions for targeted control [ 26 , 27 ]. Geostatistical methods offer powerful tools for analyzing spatially referenced data in disease ecology. Spatial autocorrelation analysis identifies clustering patterns, hotspot detection pinpoints high-risk areas, and spatial regression models account for geographic dependencies [ 28 – 30 ]. In schistosomiasis research, geostatistical approaches have been successfully applied in Kenya, Tanzania, and South Africa to map snail distributions and predict infection risk [ 31 – 33 ]. For instance, Manyangadze et al. [ 34 ] used Maxent modeling to identify suitable habitats for intermediate host snails in South Africa, while Nwoko et al. [ 35 ] employed spatial scan statistics to detect snail clusters in Nigeria. Despite these advances, few studies in Uganda have combined geostatistical analysis with snail ecology research. This gap hampers the development of spatially targeted control strategies that could improve resource allocation and intervention success [ 36 , 37 ]. The World Health Organization’s roadmap for neglected tropical diseases highlights the importance of targeted approaches based on local epidemiological and ecological data [ 38 ]. This study aimed to bridge this gap by: (1) mapping the spatial distribution of freshwater snail intermediate hosts in the Lango subregion, (2) identifying clusters of high snail density and sites with schistosome infection using geostatistical methods, (3) assessing the combined influence of spatial, ecological, and physicochemical factors on snail prevalence and density, and (4) providing evidence-based considerations for spatially targeted snail control interventions. This study represents, to our knowledge, the first application of integrated geostatistical and spatial modeling techniques to map snail intermediate hosts and identify potential transmission sites in the understudied Lango subregion of Uganda. The findings contribute to the growing body of literature on the spatial epidemiology of snail-borne diseases and support the development of integrated control strategies in northern Uganda. Methods Study area The study was conducted in Lira and Kole districts within the Lango subregion of northern Uganda (2°14′50.0″N 32°54′00.0″E). The area experiences a tropical climate with bimodal rainfall: long rains from April to May and short rains from August to October, with dry seasons from November to March and June to August [ 39 ]. Mean annual rainfall ranges from 1,200 to 1,500 mm, and temperatures range from 25°C to 30°C [ 40 ]. The landscape is characterized by extensive wetland systems, rivers (including Aswa, Akore, and Obim), swamps (Okile, Okole, Onoo, Akalo, Ayer), and rice paddies that provide suitable habitats for freshwater snails [ 41 ]. The estimated population of the Lango subregion is 2.5 million, with increasing urbanization and agricultural intensification potentially impacting aquatic ecosystems [ 42 ]. Study design and site selection A cross-sectional study was conducted during the dry (January–April) and rainy (July–November) seasons of 2023. Twenty-six sampling sites (Fig. 1 ) were selected through stratified random sampling based on: (1) habitat type (streams, ponds, swamps, rice paddies, dams), (2) presence of human water contact activities, (3) spatial distribution across the study area, and (4) accessibility. Sites were georeferenced using a Garmin eTrex 30x GPS receiver (accuracy ± 3 m). The sampling design ensured representation of different ecological zones and anthropogenic influences. Snail collection and processing At each site, snails were collected for 30 minutes by two experienced malacologists following standardized protocols [ 43 ]. Visible snails were handpicked using forceps, and submerged vegetation and substrates were sampled using a 30 × 30 cm scoop net with 2 mm mesh (supplementary file 1). Collections were made along 10-meter transects parallel to the shoreline, covering different microhabitats within each site. Snails were placed in labeled containers with site water and transported to the laboratory within 6 hours. Site-level environmental and snail collection data are provided in supplementary file 3 (Table S1 ). In the laboratory, snails were identified to species level using morphological characteristics based on standard keys [ 44 , 45 ]. Voucher specimens were preserved in 70% ethanol for verification. Live snails of the genera Biomphalaria and Bulinus were screened for schistosome infection using cercarial shedding techniques [ 46 ]. Individual snails were placed in 12-well plates with 10 ml of filtered site water and exposed to artificial light (60-watt bulb) for 6–12 hours to induce cercarial shedding. This method detects patent infections but may miss prepatent or low-intensity infections; molecular confirmation was not performed. Shed cercariae were examined under a dissecting microscope (Olympus SZ61) and identified morphologically [ 47 , 48 ]. Snails shedding mammalian-type cercariae were considered infected (Fig. 2 ). Environmental and physicochemical measurements At each sampling site, the following parameters were measured in triplicate between 8:00 and 11:00 AM: Physicochemical parameters Water temperature (°C), pH, salinity (g/L), total dissolved solids (TDS, ppm), conductivity (mS/cm), and dissolved oxygen (DO₂, mg/L) were measured using a calibrated multiparameter water quality meter (HI9829, Hanna Instruments, Sweden). Hydrological characteristics Water depth (m) was measured using a graduated pole at five points along each transect. Flow velocity (m/s) was estimated using float method in flowing waters. Water level was categorized as flooded, normal, or low based on seasonal observations. Habitat characteristics Substrate type (mud, sand, gravel, rock, concrete, peat), dominant vegetation (reeds, grasses, aquatic plants, water hyacinth, rice), and canopy cover (%) were recorded. Anthropogenic factors : Human activities at each site were documented through direct observation and categorized as: washing/bathing, car/motorcycle washing, water collection, swimming/playing, rice cultivation, or fishing. Presence of domestic animals (cattle, pigs, goats) and wild animals (water birds, wild rats, carnivores) was noted. Geographic data GPS coordinates, altitude (m), and distance to nearest settlement (m) were recorded. Geostatistical analysis Spatial autocorrelation Global spatial autocorrelation in snail density and infection prevalence was assessed using Moran’s I statistic [ 49 ]. A row-standardized inverse distance weight matrix with a 2 km bandwidth was used based on estimated snail dispersal distances [ 50 ]. Significance was tested using 999 permutations, and Variogram plots were used to characterise spatial dependence. Hotspot analysis Local clusters of high snail density (hotspots) and low density (coldspots) were identified using the Getis-Ord Gi* statistic [ 51 ]. Sites with Gi* > 1.96 (p < 0.05) were classified as hotspots, and those with Gi* < -1.645 (p < 0.10) as coldspots. Spatial interpolation Continuous surfaces of snail density and key physicochemical parameters were generated using inverse distance weighting (IDW) interpolation [ 52 ]. Statistical modeling Model specification Two types of models were developed and compared: Non-spatial models Generalized linear models (GLMs) with binomial distribution for prevalence (logit link) and negative binomial distribution for density (log link). Spatial models Generalized linear mixed models (GLMMs) incorporating Gaussian spatial random effects to account for autocorrelation [ 53 ]. Predictor variables Initial candidate predictors included: physicochemical parameters (temperature, pH, salinity, TDS, conductivity, DO₂), hydrological factors (water depth, flow rate, water level), habitat characteristics (substrate, vegetation, canopy cover), anthropogenic factors (human activities, domestic/wild animals), geographic variables (altitude, distance to settlement), and season. Continuous variables were standardized (z-scores) for analysis. Model selection and validation Variable selection was performed using backward elimination based on Akaike Information Criterion (AIC) [ 54 ]. Multicollinearity was assessed using variance inflation factors (VIF < 5) [ 55 ]. For spatial models, variogram analysis was used to estimate spatial correlation range. Model performance was evaluated using: (1) AIC comparison between spatial and non-spatial models, (2) pseudo-R² for GLMMs [ 56 ], (3) residual spatial autocorrelation testing using Moran’s I on residuals, and (4) cross-validation with 70% training and 30% testing data. Data analysis All analyses were conducted in R 4.3.1 [ 57 ] using packages: spatstat for spatial point pattern analysis [ 58 ], gstat for geostatistics [ 59 ], spaMM for spatial GLMMs [ 60 ], spdep for spatial autocorrelation [ 61 ], and ggplot2 for visualization [ 62 ]. Hotspot mapping and interpolation surfaces were created in QGIS 3.28 [ 63 ]. Ethical considerations The study protocol was approved by the Gulu University Research Ethics Committee (GUREC-2022-323) and Uganda National Council for Science and Technology (UNCST-HS2571ES). Community consent was obtained through local leaders before sampling. No personal identifying information was collected from individuals using water bodies. Results Snail species composition and distribution A total of 4,802 freshwater snails belonging to 13 species were collected from 26 sampling sites during both dry and rainy seasons of 2023 (supplementary file 2). The species composition and seasonal distribution are presented in Table 1 . The three most abundant genera were Biomphalaria (47.2%, n = 2,266), Bulinus (23.7%, n = 1,138), and Lymnaea (10.6%, n = 510). Biomphalaria choanomphala was the most abundant species (25.8%, n = 1,241), followed by Bulinus africanus (17.7%, n = 852) and Biomphalaria sudanica (15.6%, n = 749). Table 1 Species composition and seasonal distribution of freshwater snails in the Lango subregion, northern Uganda (n = 4,802) Species Dry season (n) Rainy season (n) Total (n) % of total Biomphalaria choanomphala 794 447 1241 25.8 Bulinus africanus 431 421 852 17.7 Biomphalaria sudanica 119 630 749 15.6 Lymnaea natalensis 278 232 510 10.6 Pila ovata 125 293 418 8.7 Lanistes carinatus 86 272 358 7.5 Biomphalaria pfeifferi 187 89 276 5.7 Bulinus ugandae 137 105 242 5.04 Melanoides tuberculata 16 83 99 2.1 Bulinus globosus 33 1 34 0.7 Bivalves 13 0 13 0.3 Bulinus forskalii 1 6 7 0.2 Bulinus nasutus 3 0 3 0.1 Total 2223 2579 4802 100 Detailed site-level data, including coordinates, habitat types, and physicochemical measurements, are available in supplementary file 3 (Table S1 ). Seasonal analysis revealed higher overall snail abundance during the rainy season (53.7%) compared to the dry season (46.3%). However, species richness was greater in the dry season (13 species) than the rainy season (11 species). Bulinus nasutus and bivalves were collected exclusively during the dry season. Wilcoxon signed-rank tests confirmed significant seasonal differences in abundance for several species: Lymnaea natalensis was more abundant in the dry season (Z = -3.064, p = 0.002), while Biomphalaria choanomphala (Z = -2.076, p = 0.038), B. pfeifferi (Z = -3.379, p = 0.001), and Pila ovata (Z = -2.719, p < 0.001) were more abundant in the rainy season. Spatial patterns and clustering Spatial analysis revealed significant clustering of snail populations across the study area. Global Moran’s I statistics indicated positive spatial autocorrelation for total snail density (I = 0.28, p = 0.008) and for key species: Biomphalaria choanomphala (I = 0.32, p = 0.004), B. sudanica (I = 0.24, p = 0.018), and Bulinus africanus (I = 0.19, p = 0.042). This suggests that snail populations are not randomly distributed but clustered in specific areas. Variogram diagnostics supporting spatial dependence are shown in (Fig. 2 ). Empirical semivariograms (blue points) and fitted exponential variogram models (orange curves) illustrate spatial autocorrelation in snail metrics across the georeferenced sampling sites. The x-axis displays lag distance (km), and the y-axis shows semivariance. The panels depict: (A) total snails collected (count), (B) Biomphalaria choanomphala density (snails/m²), (C) Biomphalaria sudanica density (snails/m²), and (D) infected snails (count) (infection status as detailed in the Methods). Estimated exponential model parameters are: (A) nugget = 1.19×10³, partial sill = 2.58×10⁴, range = 9.24 km; (B) nugget = 1.23×10³, partial sill = 1.00×10⁴, range = 13.18 km; (C) nugget = 2.33×10⁻¹¹, partial sill = 2.25×10³, range = 3.93 km; (D) nugget = 2.3, partial sill = 0.48, range = 6.89 km. The range indicates the approximate distance over which observations remain spatially correlated. Getis-Ord Gi* hotspot analysis identified three significant clusters of high snail density (hotspots) and two coldspots (Fig. 3 ). The primary hotspot (Cluster A) was located in the Okile swamp and surrounding rice paddies (Z = 3.12, p = 0.002), covering approximately 4.2 km². Secondary hotspots included the Aswa River confluence area (Cluster B: Z = 2.89, p = 0.004) and Apoka swamp system (Cluster C: Z = 2.45, p = 0.014). Coldspots were identified in areas with sandy substrates and high human disturbance. Map showing Getis-Ord Gi* statistics for snail density clusters. Red areas indicate significant hotspots (Z > 1.96, p < 0.05), blue areas indicate coldspots (Z < -1.645, p < 0.10), and yellow areas indicate non-significant zones. Sampling sites are shown as black dots. Inset shows location of study area within Uganda. Schistosome infection rates and spatial distribution Among the 3,404 Biomphalaria and Bulinus snails examined for cercarial shedding, only 5 (0.15%) were infected with human schistosome cercariae. Infection rates by species were: Biomphalaria sudanica 0.03% (1/749), B. choanomphala 0.06% (2/1241), and Bulinus africanus 0.06% (2/852). No infections were detected in other snail species. Spatial analysis of infection indicated one site (Abolet rice field area) where infected snails showed clustering (Z = 2.78, p = 0.012). Infected snails were collected from three distinct locations: Abolet rice field (Lira District, rainy season), Apoka swamp (Lira District, dry season), and Telela rice field (Lira City, dry season). The spatial distribution of infected snails showed clustering within 1.5 km radius of rice cultivation areas. Physicochemical and environmental characteristics Table 2 presents descriptive statistics for physicochemical parameters measured during the study. Water temperature ranged from 26.3°C to 34.8°C (mean ± SD: 29.98 ± 2.36°C), with higher temperatures during the dry season. pH values were generally alkaline (range: 6.4–9.6, mean: 7.90 ± 0.60). Salinity was low overall (0.0–0.2 g/L, mean: 0.01 ± 0.04 g/L) but showed seasonal variation. TDS ranged widely from 42 to 906 ppm (mean: 171.46 ± 158.27 ppm), with higher values in the rainy season likely due to increased runoff. Dissolved oxygen showed considerable variation (1.6–50.3 mg/L, mean: 19.98 ± 14.39 mg/L), with higher values in the dry season. Table 2 Descriptive statistics of physicochemical parameters by season Parameter Dry season Mean ± SD (Range) Rainy season Mean ± SD (Range) Overall Mean ± SD (Range) Temperature (°C) 29.05 ± 2.12 (26.3–33.1) 30.91 ± 2.20 (27.5–34.8) 29.98 ± 2.36 (26.3–34.8) pH 7.73 ± 0.55 (6.4–8.9) 8.08 ± 0.57 (7.1–9.6) 7.90 ± 0.60 (6.4–9.6) Salinity (g/L) 0.02 ± 0.05 (0.0–0.2) 0.00 ± 0.01 (0.0–0.05) 0.01 ± 0.04 (0.0–0.2) TDS (ppm) 151.54 ± 125.33 (42–580) 191.38 ± 183.56 (58–906) 171.46 ± 158.27 (42–906) Conductivity (mS/cm) 0.24 ± 0.20 (0.01–0.75) 0.30 ± 0.35 (0.04–1.68) 0.27 ± 0.29 (0.01–1.68) DO₂ (mg/L) 29.31 ± 12.45 (12.5–50.3) 10.64 ± 5.12 (1.6–22.4) 19.98 ± 14.39 (1.6–50.3) Habitat characteristics varied across sites: 80.8% had muddy substrates, 53.8% had water depths of 0.5–1 m, and 40.4% had flow rates of 0.5–1 m/s. Domestic animals (primarily cattle) were present at 96.2% of sites, while water birds were observed at 67.3% of sites. Anthropogenic activities were common, with washing/bathing occurring at 65.4% of sites and rice cultivation at 30.8% of sites. Predictors of snail prevalence: Spatial vs. non-spatial models Table 3 presents results from logistic regression models predicting snail prevalence. For Biomphalaria sudanica , the spatial GLMM explained 73.1% of variation (pseudo-R² = 0.731), significantly outperforming the non-spatial GLM (ΔAIC = 15.3). Key predictors included salinity (OR = 0.21, 95% CI: 0.09–0.49, p < 0.001), TDS (OR = 0.87, 95% CI: 0.79–0.96, p = 0.005), conductivity (OR = 0.31, 95% CI: 0.14–0.69, p = 0.004), and water depth (0.5–1 m vs. <0.5 m: OR = 0.42, 95% CI: 0.21–0.84, p = 0.014). The spatial random effect variance was 0.86, accounting for 22% of total variance. For Biomphalaria choanomphala , the spatial model explained 82.2% of variation (pseudo-R² = 0.822, ΔAIC = 12.7). Significant predictors included dissolved oxygen (OR = 1.18, 95% CI: 1.05–1.33, p = 0.006), presence in ponds (OR = 4.56, 95% CI: 1.89–11.01, p < 0.001), rice paddies (OR = 3.78, 95% CI: 1.42–10.07, p = 0.008), and washing/bathing activities (OR = 2.34, 95% CI: 1.28–4.28, p = 0.006). Car washing showed a negative association (OR = 0.41, 95% CI: 0.19–0.88, p = 0.022). Table 3 Logistic regression models for snail prevalence: comparison of spatial GLMMs and non-spatial GLMs Species & Model Predictor Odds Ratio (95% CI) p-value AIC Pseudo-R² B. sudanica (GLM) Salinity 0.35 (0.17–0.72) 0.004 127.4 0.521 TDS 0.90 (0.83–0.98) 0.014 B. sudanica (GLMM) Salinity 0.21 (0.09–0.49) < 0.001 112.1 0.731 TDS 0.87 (0.79–0.96) 0.005 Conductivity 0.31 (0.14–0.69) 0.004 Water depth (0.5–1m) 0.42 (0.21–0.84) 0.014 Spatial variance (σ²) 0.86 B. choanomphala (GLM) DO₂ 1.12 (1.01–1.24) 0.032 98.7 0.654 Pond habitat 3.89 (1.65–9.18) 0.002 B. choanomphala (GLMM) DO₂ 1.18 (1.05–1.33) 0.006 86.0 0.822 Pond habitat 4.56 (1.89–11.01) < 0.001 Rice paddy 3.78 (1.42–10.07) 0.008 Washing/bathing 2.34 (1.28–4.28) 0.006 Car washing 0.41 (0.19–0.88) 0.022 Spatial variance (σ²) 1.12 Predictors of snail density: Spatial vs. non-spatial models Table 4 presents negative binomial regression results for snail density. For Biomphalaria sudanica , the spatial model (ΔAIC = 14.2) identified positive associations with water temperature (IRR = 1.41, 95% CI: 1.22–1.64, p < 0.001), pH (IRR = 2.72, 95% CI: 1.76–4.20, p < 0.001), TDS (IRR = 1.01, 95% CI: 1.00–1.01, p = 0.002), and washing activities (IRR = 4.62, 95% CI: 2.14–9.98, p < 0.001). Negative associations were found with dissolved oxygen (IRR = 0.94, 95% CI: 0.90–0.98, p = 0.003) and dry season (IRR = 0.19, 95% CI: 0.09–0.40, p < 0.001). For Biomphalaria choanomphala , density increased with dissolved oxygen (IRR = 1.07, 95% CI: 1.03–1.11, p < 0.001) and presence in streams (IRR = 5.07, 95% CI: 2.44–10.53, p < 0.001), but decreased with temperature (IRR = 0.50, 95% CI: 0.38–0.66, p < 0.001), TDS (IRR = 0.96, 95% CI: 0.94–0.98, p < 0.001), and conductivity (IRR = 0.28, 95% CI: 0.15–0.52, p < 0.001). Table 4 Negative binomial regression models for snail density: comparison of spatial GLMMs and non-spatial GLMs Species & Model Predictor Incidence Rate Ratio (95% CI) p-value AIC B. sudanica (GLM) Temperature 1.32 (1.15–1.52) < 0.001 345.2 pH 2.45 (1.62–3.70) < 0.001 Washing activity 3.85 (1.85–8.01) < 0.001 B. sudanica (GLMM) Temperature 1.41 (1.22–1.64) < 0.001 331.0 pH 2.72 (1.76–4.20) < 0.001 TDS 1.01 (1.00–1.01) 0.002 DO₂ 0.94 (0.90–0.98) 0.003 Washing activity 4.62 (2.14–9.98) < 0.001 Dry season 0.19 (0.09–0.40) < 0.001 Spatial variance (σ²) 0.74 B. choanomphala (GLM) DO₂ 1.05 (1.01–1.09) 0.012 312.8 Stream habitat 4.12 (2.01–8.45) < 0.001 B. choanomphala (GLMM) DO₂ 1.07 (1.03–1.11) < 0.001 299.1 Stream habitat 5.07 (2.44–10.53) < 0.001 Temperature 0.50 (0.38–0.66) < 0.001 TDS 0.96 (0.94–0.98) < 0.001 Conductivity 0.28 (0.15–0.52) < 0.001 Spatial variance (σ²) 0.89 Spatial interpolation of key parameters Figure 4 presents IDW interpolation surfaces for Biomphalaria choanomphala density and key environmental predictors. High-density areas corresponded with regions of moderate TDS (150–250 ppm), neutral to slightly alkaline pH (7.5–8.0), and proximity to human settlements. The interpolation revealed a gradient of decreasing snail density from central wetland areas toward peripheral regions with sandy substrates and lower vegetation cover. Four-panel figure showing inverse distance weighting interpolation surfaces for: (A) Biomphalaria choanomphala density (snails/m²), (B) Total dissolved solids (ppm), (C) pH, and (D) Distance to nearest settlement (km). Warmer colors indicate higher values. Cross symbols show sampling locations. Model validation and performance Spatial models consistently outperformed non-spatial counterparts across all response variables (mean ΔAIC = 13.4, range: 8.3–18.1). Residual spatial autocorrelation was significantly reduced in spatial models (Moran’s I on residuals: -0.03 to 0.11, p > 0.05) compared to non-spatial models (Moran’s I: 0.24–0.38, p < 0.05). Cross-validation showed better predictive accuracy for spatial models (mean absolute error reduction: 18–32% across models). Discussion This study represents one of the first comprehensive integrations of geostatistical analysis with ecological modeling of freshwater snail intermediate hosts in northern Uganda. Our findings demonstrate significant spatial structuring of snail populations, with clear hotspots of high density and sites where infection was detected that traditional non-spatial approaches would miss. The spatial GLMMs consistently outperformed their non-spatial counterparts, explaining 18–24% more variance by incorporating spatial random effects. The overall infection rate (0.15%) is lower than rates reported from the Lake Victoria basin (0.5–2.8%) [ 64 , 65 ], but it aligns with findings from other inland regions of Uganda [ 66 , 67 ]. This difference may arise from variations in transmission intensity, human water contact practices, or snail compatibility with local schistosome strains [ 68 , 69 ]. The clustering of infected snails in rice paddies near settlements emphasizes the importance of agricultural water management in creating potential transmission sites, consistent with studies from Tanzania and Kenya [ 70 , 71 ]. The identified hotspots in Okile swamp, Aswa river confluence, and Apoka swamp exhibit common features: moderate TDS (150–250 ppm), neutral to slightly alkaline pH (7.5–8.0), muddy substrates, and proximity to human settlements with frequent water contact activities. These conditions match the known ecological preferences of Biomphalaria and Bulinus species [ 72 , 73 ]. The negative correlation between car washing and snail prevalence/density suggests that chemical pollutants or physical disturbance from this activity may harm snail habitats, a finding supported by studies in Nigeria and South Africa [ 74 , 75 ]. Seasonal variations in snail abundance followed expected patterns, with higher densities during the rainy season due to expanded habitats and increased nutrient inputs [ 76 , 77 ]. However, the greater species richness during the dry season may reflect concentration effects as water bodies contract, making snails easier to sample. This has implications for control timing, suggesting that dry-season interventions may be more effective for reducing snail populations. The spatial autocorrelation detected for key species (Moran’s I = 0.19–0.32) indicates that snail populations are not randomly distributed but exhibit patchiness at scales of 1–3 km. This clustering likely results from both environmental heterogeneity (habitat suitability gradients) and limited dispersal capabilities of snails [ 78 , 79 ]. Similar spatial patterns have been reported for snail populations in Kenya and Tanzania [ 80 , 81 ]. Anthropogenic activities exhibited complex spatial relationships with snail distributions. Washing and bathing activities generally increased snail density, possibly due to nutrient enrichment from soap and organic matter [ 82 , 83 ]. Conversely, car washing and swimming/playing were linked to adverse effects, likely owing to habitat disturbance and chemical inputs [ 84 ]. These findings highlight the importance of context-specific interventions that consider local water-use practices. The superior performance of spatial models (ΔAIC = 8.3–18.1) highlights the importance of considering spatial autocorrelation in ecological studies of snail distributions. Ignoring spatial dependencies can result in biased parameter estimates, increased Type I errors, and poor predictions for targeted control [ 85 , 86 ]. Our spatial GLMMs effectively captured both fixed effects of environmental predictors and random spatial effects, offering more reliable inference. Study limitations Several limitations should be acknowledged when interpreting our findings. First, the cross-sectional design offers only a snapshot of snail distributions and infection rates. Long-term sampling over multiple years would better capture temporal dynamics and seasonal changes. Second, although we included a comprehensive range of environmental predictors, unmeasured factors such as predation pressure, competition, and microhabitat characteristics could influence snail distributions. Third, the moderate spatial resolution (26 sites across approximately 2,000 km²) may not capture fine-scale heterogeneity, especially in complex wetland systems. However, our stratified random sampling was explicitly designed to capture major habitat types across the study area, and the hotspot analysis successfully identified significant risk clusters despite this potential limitation. Fourth, cercarial shedding has limited sensitivity for detecting prepatent infections, which might lead to an underestimation of true infection rates. Additionally, infection detection relied on cercarial shedding without molecular confirmation, which may underestimate true infection rates and limit species-specific identification of schistosomes. Molecular methods would enable more accurate detection of infections [ 87 , 88 ]. Finally, our study area reflects the specific ecological conditions of the Lango subregion; therefore, findings might not be directly generalizable to regions with different hydrological or land-use features. Implications for control The spatial hotspots identified in this study serve as potential priority targets for snail control measures. Combining approaches such as environmental modification (habitat disturbance in hotspot areas), chemical control (targeted mollusciciding), biological control (duck introduction in rice paddies), and community-based sanitation improvements could be considered in these high-risk zones [ 89 , 90 ]. The spatial risk maps produced can assist district health authorities in planning targeted mass drug administration and health education campaigns [ 91 , 92 ]. Future research should focus on: (1) longitudinal monitoring of identified hotspots to understand transmission dynamics, (2) experimental manipulation of key environmental factors to test causality, (3) integration of remote sensing data for larger-scale habitat mapping, and (4) community participatory approaches to develop context-appropriate interventions [ 93 – 95 ]. Conclusions This study highlights the significant value of geostatistical approaches in understanding the spatial ecology of schistosomiasis intermediate host snails in northern Uganda. The use of spatial clustering analysis, hotspot mapping, and spatial regression modelling identified distinct, high-risk snail density clusters mainly located in rice paddies and swamps near human settlements, which would be overlooked by traditional non-spatial methods. The demonstrated effectiveness of spatial models, combined with key environmental and anthropogenic predictors, provides a strong evidence base for guiding consideration of targeted control measures. Our findings support the consideration of spatially explicit interventions, such as targeted snail control in high-density areas, community-led habitat modification, and the integration of risk maps into health planning, which could improve the efficiency of schistosomiasis control efforts in Uganda and similar endemic areas. Abbreviations AIC Akaike Information Criterion CI Confidence Interval DO₂ Dissolved Oxygen GLM Generalized Linear Model GLMM Generalized Linear Mixed Model GPS Global Positioning System IDW Inverse Distance Weighting IRR Incidence Rate Ratio OR Odds Ratio SD Standard Deviation TDS Total Dissolved Solids VIF Variance Inflation Factor Declarations Ethics approval and consent to participate This study was approved by the Gulu University Research Ethics Committee (reference: GUREC-2022-323) and the Uganda National Council for Science and Technology (reference: UNCST-HS2571ES). Permission was obtained from district and local authorities before commencing the study. Community consent was obtained through meetings with local leaders and community representatives. Individual consent was not required as the study did not involve human subjects directly, only environmental sampling and snail collection. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted as part of academic research requirements at Gulu University. Authors' contributions JPB: Conceptualization, study design, fieldwork, data collection, laboratory analysis, statistical analysis, spatial analysis, manuscript writing, revision. RO: Methodology, supervision, validation, manuscript review. MN: Fieldwork, data collection, supervision, laboratory analysis, data curation. HA: Methodology, manuscript review. GMM: Statistical analysis, methodology, validation. RE: Methodology, manuscript review. EIOA: Supervision, conceptualization, validation, manuscript review. All authors read and approved the final manuscript. Acknowledgements We thank the District Health Officers of Lira and Kole districts for facilitating the study. We acknowledge the technical support provided by the Vector Control Division, Ministry of Health Uganda. We are grateful to the village health teams and community members who assisted with fieldwork and site access. We thank the laboratory technicians at Gulu University for assistance with snail processing. Finally, we acknowledge the Uganda National Meteorological Authority for providing climate data. Availability of data and materials All data generated or analysed during this study are included in this published article and its supplementary information files. References Brown DS. Freshwater snails of Africa and their medical importance. 2nd ed. London: Taylor & Francis; 1994. Colley DG, Bustinduy AL, Secor WE, King CH. Human schistosomiasis. Lancet. 2014;383(9936):2253–64. World Health Organization. Schistosomiasis: key facts. Geneva: World Health Organization; 2022. Adriko M, Tinkitina B, Tukahebwa EM, Standley CJ, Stothard JR, Kabatereine NB. 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Disease ecology, health and the environment: a framework to account for ecological and socio-economic drivers in the control of neglected tropical diseases. Philos Trans R Soc Lond B Biol Sci. 2017;372(1722):20160128. Bärenbold O, Garba A, Colley DG, Fleming FM, Haggag AA, Ramzy RMR, et al. Translating preventive chemotherapy prevalence thresholds for Schistosoma mansoni from the Kato-Katz technique into the point-of-care circulating cathodic antigen diagnostic test. PLoS Negl Trop Dis. 2018;12(12): e0006941. Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile1.docx Additional file 1: Figure S1. Photographs of sampling sites, snail counting, identification, and cercarial shedding techniques Supplementaryfile2.docx Additional file 2: Figure S2. Photographs of intermediate freshwater snails collected and identified morphologically to species levels. Supplementaryfile3.docx Additional file 3: Table S1. Site-level environmental and snail collection data. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Mar, 2026 Reviewers agreed at journal 20 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers invited by journal 02 Mar, 2026 Submission checks completed at journal 03 Feb, 2026 First submitted to journal 31 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8643236","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600660754,"identity":"613a497b-d16a-4b83-8a0a-da6c914a1dde","order_by":0,"name":"John Paul Byagamy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACxgYILcPPzNhw4AOQxcZOpBYeyfbmxoczQFqYibSNx+DM8WZjHhCTkBbm9t6DjwtqDvMw3Ehsk7b5tU2ej5mB8cPHHDwO6zmXbDzj2GEexhlALbl9tw3bmBmYJWduw6NlRo6ZNA/bYR5mCZCWntuMQC1szLz4tZj/5vl3mIcNpMWy57Y9MVrMmHnbDvPw8BxsNmb4cTuRsJaeM8bSM/vSeSTYGxsf9jbcTm5jZmzG6xfD9h7DzwXfrOXsD7M/OPDjz23b+e3NBz98xKelATkiGNvAZANu9UAgz4ASd3/wKh4Fo2AUjIIRCgCJ4E/3FlEpOQAAAABJRU5ErkJggg==","orcid":"","institution":"Gulu University","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"Paul","lastName":"Byagamy","suffix":""},{"id":600660757,"identity":"b078dac5-840c-49bb-8bfb-f6faec70c714","order_by":1,"name":"Robert Opiro","email":"","orcid":"","institution":"Gulu University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Opiro","suffix":""},{"id":600660758,"identity":"212a2114-d1db-4305-8a75-77ca4005dd59","order_by":2,"name":"Margaret Nyafwono","email":"","orcid":"","institution":"Gulu University","correspondingAuthor":false,"prefix":"","firstName":"Margaret","middleName":"","lastName":"Nyafwono","suffix":""},{"id":600660759,"identity":"63324826-f4d3-4857-a6c8-edd82e86ec5b","order_by":3,"name":"Harriet Angwech","email":"","orcid":"","institution":"Gulu University","correspondingAuthor":false,"prefix":"","firstName":"Harriet","middleName":"","lastName":"Angwech","suffix":""},{"id":600660761,"identity":"f4804a1c-474a-4b62-be35-4da82c8e304b","order_by":4,"name":"Geoffrey Maxwell Malinga","email":"","orcid":"","institution":"Gulu University","correspondingAuthor":false,"prefix":"","firstName":"Geoffrey","middleName":"Maxwell","lastName":"Malinga","suffix":""},{"id":600660762,"identity":"6bf34b18-2e96-4a2b-a46f-dd401642fe9b","order_by":5,"name":"Richard Echodu","email":"","orcid":"","institution":"Gulu University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Echodu","suffix":""},{"id":600660763,"identity":"28825fec-209f-46c0-9588-2f70390861a7","order_by":6,"name":"Emmanuel Igwaro Odongo-Aginya","email":"","orcid":"","institution":"Gulu University","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"Igwaro","lastName":"Odongo-Aginya","suffix":""}],"badges":[],"createdAt":"2026-01-19 22:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8643236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8643236/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104011537,"identity":"c4909b65-f4fb-4649-8e5b-b81800eecedc","added_by":"auto","created_at":"2026-03-05 15:57:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97767,"visible":true,"origin":"","legend":"\u003cp\u003eThe study area and sampled sites in Lira city, Lira district \u0026amp; Kole district, Lango subregion, northern Uganda\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/46b9d16dad280659098c2aef.png"},{"id":104011541,"identity":"aa96e8a5-cd83-4f5c-8091-bfaedbce6a9f","added_by":"auto","created_at":"2026-03-05 15:57:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57241,"visible":true,"origin":"","legend":"\u003cp\u003eVariogram plots (exponential models) for the spatial analysis of freshwater snails and infection.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/a02c7b38d682d92903cb0b2d.png"},{"id":104011538,"identity":"e399c5e9-246b-4607-a6f8-dfdf71009dd3","added_by":"auto","created_at":"2026-03-05 15:57:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62093,"visible":true,"origin":"","legend":"\u003cp\u003eHotspot analysis of freshwater snail density in the Lango subregion, northern Uganda.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/0df579153aafa638d5dbdd3e.png"},{"id":104011540,"identity":"023c3648-c653-4491-94e5-3587840fcda7","added_by":"auto","created_at":"2026-03-05 15:57:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96766,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial interpolation of snail density and environmental predictors.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/7559305712be8b55a7ab96a5.png"},{"id":104402843,"identity":"e84cc41e-aaf6-445c-baa1-e84d36680c24","added_by":"auto","created_at":"2026-03-11 12:16:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1884068,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/e26f8f1f-ccbe-4fcd-887d-45b13879564e.pdf"},{"id":104011542,"identity":"bf8bc92b-0703-4904-80a4-9104985eb39f","added_by":"auto","created_at":"2026-03-05 15:57:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4034966,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Figure S1. Photographs of sampling sites, snail counting, identification, and cercarial shedding techniques\u003c/p\u003e","description":"","filename":"Supplementaryfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/1f90ec5881c1eba9efcb471b.docx"},{"id":104011543,"identity":"453cca49-eb57-481c-ad42-5d553e6aeddc","added_by":"auto","created_at":"2026-03-05 15:57:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2029482,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Figure S2. Photographs of intermediate freshwater snails collected and identified morphologically to species levels.\u003c/p\u003e","description":"","filename":"Supplementaryfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/247fff021b250f592afe10b9.docx"},{"id":104011539,"identity":"ae00bd0b-687c-49b6-9521-83dbeff97223","added_by":"auto","created_at":"2026-03-05 15:57:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17853,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Table S1. Site-level environmental and snail collection data.\u003c/p\u003e","description":"","filename":"Supplementaryfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-8643236/v1/30b324a6ed48168d20ae3c11.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatio-ecological determinants of freshwater snail intermediate hosts and schistosome infection in the Lango subregion, Northern Uganda: a geostatistical approach to targeted disease control","fulltext":[{"header":"Background","content":"\u003cp\u003eFreshwater snails of the class Gastropoda play crucial ecological roles in aquatic ecosystems but also serve as intermediate hosts for medically important trematodes, particularly those causing schistosomiasis and fascioliasis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Globally, schistosomiasis affects approximately 250\u0026nbsp;million people, with over 90% of cases occurring in sub-Saharan Africa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In Uganda, schistosomiasis is endemic in approximately 81 districts, with an estimated 5.4\u0026nbsp;million people infected and 14.7\u0026nbsp;million at risk [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The disease burden is particularly high among school-aged children in endemic areas, leading to anemia, impaired growth, and reduced cognitive development [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe transmission dynamics of snail-borne diseases are closely connected to the distribution and abundance of specific freshwater snail intermediate hosts. \u003cem\u003eBiomphalaria\u003c/em\u003e species transmit \u003cem\u003eSchistosoma mansoni\u003c/em\u003e (intestinal schistosomiasis), \u003cem\u003eBulinus\u003c/em\u003e species transmit \u003cem\u003eS. haematobium\u003c/em\u003e (urogenital schistosomiasis), and \u003cem\u003eLymnaea\u003c/em\u003e species transmit \u003cem\u003eFasciola\u003c/em\u003e spp. (fascioliasis) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These snails thrive in various freshwater habitats including streams, ponds, swamps, and irrigation schemes, with their distribution affected by complex interactions between physicochemical factors, ecological conditions, and human activities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies in Uganda have mainly focused on the Lake Victoria basin, where high snail densities and infection rates have been documented [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, northern Uganda features distinct ecological and climatic conditions, characterized by seasonal rainfall patterns, extensive wetland systems, and different land use practices [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The Lango subregion, with about 30% of its area covered by swamps, rivers, and wetlands, presents a unique transmission setting that remains understudied [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Recent urbanization and agricultural expansion, especially rice cultivation, have altered aquatic habitats, potentially creating favorable conditions for snail proliferation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEcological studies have identified various factors influencing snail distributions, including water temperature, pH, dissolved oxygen, conductivity, vegetation cover, and substrate type [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, these factors often exhibit spatial heterogeneity, creating patchy distributions of snail populations and disease transmission risk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Traditional regression approaches that ignore spatial autocorrelation may produce biased estimates and inadequate predictions for targeted control [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGeostatistical methods offer powerful tools for analyzing spatially referenced data in disease ecology. Spatial autocorrelation analysis identifies clustering patterns, hotspot detection pinpoints high-risk areas, and spatial regression models account for geographic dependencies [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In schistosomiasis research, geostatistical approaches have been successfully applied in Kenya, Tanzania, and South Africa to map snail distributions and predict infection risk [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. For instance, Manyangadze et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] used Maxent modeling to identify suitable habitats for intermediate host snails in South Africa, while Nwoko et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] employed spatial scan statistics to detect snail clusters in Nigeria.\u003c/p\u003e \u003cp\u003eDespite these advances, few studies in Uganda have combined geostatistical analysis with snail ecology research. This gap hampers the development of spatially targeted control strategies that could improve resource allocation and intervention success [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The World Health Organization\u0026rsquo;s roadmap for neglected tropical diseases highlights the importance of targeted approaches based on local epidemiological and ecological data [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study aimed to bridge this gap by: (1) mapping the spatial distribution of freshwater snail intermediate hosts in the Lango subregion, (2) identifying clusters of high snail density and sites with schistosome infection using geostatistical methods, (3) assessing the combined influence of spatial, ecological, and physicochemical factors on snail prevalence and density, and (4) providing evidence-based considerations for spatially targeted snail control interventions. This study represents, to our knowledge, the first application of integrated geostatistical and spatial modeling techniques to map snail intermediate hosts and identify potential transmission sites in the understudied Lango subregion of Uganda.\u003c/p\u003e \u003cp\u003eThe findings contribute to the growing body of literature on the spatial epidemiology of snail-borne diseases and support the development of integrated control strategies in northern Uganda.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study was conducted in Lira and Kole districts within the Lango subregion of northern Uganda (2\u0026deg;14\u0026prime;50.0\u0026Prime;N 32\u0026deg;54\u0026prime;00.0\u0026Prime;E). The area experiences a tropical climate with bimodal rainfall: long rains from April to May and short rains from August to October, with dry seasons from November to March and June to August [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Mean annual rainfall ranges from 1,200 to 1,500 mm, and temperatures range from 25\u0026deg;C to 30\u0026deg;C [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The landscape is characterized by extensive wetland systems, rivers (including Aswa, Akore, and Obim), swamps (Okile, Okole, Onoo, Akalo, Ayer), and rice paddies that provide suitable habitats for freshwater snails [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The estimated population of the Lango subregion is 2.5\u0026nbsp;million, with increasing urbanization and agricultural intensification potentially impacting aquatic ecosystems [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy design and site selection\u003c/h3\u003e\n\u003cp\u003eA cross-sectional study was conducted during the dry (January\u0026ndash;April) and rainy (July\u0026ndash;November) seasons of 2023. Twenty-six sampling sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) were selected through stratified random sampling based on: (1) habitat type (streams, ponds, swamps, rice paddies, dams), (2) presence of human water contact activities, (3) spatial distribution across the study area, and (4) accessibility. Sites were georeferenced using a Garmin eTrex 30x GPS receiver (accuracy\u0026thinsp;\u0026plusmn;\u0026thinsp;3 m). The sampling design ensured representation of different ecological zones and anthropogenic influences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSnail collection and processing\u003c/h3\u003e\n\u003cp\u003eAt each site, snails were collected for 30 minutes by two experienced malacologists following standardized protocols [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Visible snails were handpicked using forceps, and submerged vegetation and substrates were sampled using a 30 \u0026times; 30 cm scoop net with 2 mm mesh (supplementary file 1). Collections were made along 10-meter transects parallel to the shoreline, covering different microhabitats within each site. Snails were placed in labeled containers with site water and transported to the laboratory within 6 hours. Site-level environmental and snail collection data are provided in supplementary file 3 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the laboratory, snails were identified to species level using morphological characteristics based on standard keys [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Voucher specimens were preserved in 70% ethanol for verification. Live snails of the genera \u003cem\u003eBiomphalaria\u003c/em\u003e and \u003cem\u003eBulinus\u003c/em\u003e were screened for schistosome infection using cercarial shedding techniques [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Individual snails were placed in 12-well plates with 10 ml of filtered site water and exposed to artificial light (60-watt bulb) for 6\u0026ndash;12 hours to induce cercarial shedding. This method detects patent infections but may miss prepatent or low-intensity infections; molecular confirmation was not performed. Shed cercariae were examined under a dissecting microscope (Olympus SZ61) and identified morphologically [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Snails shedding mammalian-type cercariae were considered infected (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEnvironmental and physicochemical measurements\u003c/h3\u003e\n\u003cp\u003eAt each sampling site, the following parameters were measured in triplicate between 8:00 and 11:00 AM:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePhysicochemical parameters\u003c/strong\u003e \u003cp\u003eWater temperature (\u0026deg;C), pH, salinity (g/L), total dissolved solids (TDS, ppm), conductivity (mS/cm), and dissolved oxygen (DO₂, mg/L) were measured using a calibrated multiparameter water quality meter (HI9829, Hanna Instruments, Sweden).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHydrological characteristics\u003c/strong\u003e \u003cp\u003eWater depth (m) was measured using a graduated pole at five points along each transect. Flow velocity (m/s) was estimated using float method in flowing waters. Water level was categorized as flooded, normal, or low based on seasonal observations.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHabitat characteristics\u003c/strong\u003e \u003cp\u003eSubstrate type (mud, sand, gravel, rock, concrete, peat), dominant vegetation (reeds, grasses, aquatic plants, water hyacinth, rice), and canopy cover (%) were recorded.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAnthropogenic factors\u003c/b\u003e: Human activities at each site were documented through direct observation and categorized as: washing/bathing, car/motorcycle washing, water collection, swimming/playing, rice cultivation, or fishing. Presence of domestic animals (cattle, pigs, goats) and wild animals (water birds, wild rats, carnivores) was noted.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eGeographic data\u003c/strong\u003e \u003cp\u003eGPS coordinates, altitude (m), and distance to nearest settlement (m) were recorded.\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003eGeostatistical analysis\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSpatial autocorrelation\u003c/h2\u003e \u003cp\u003eGlobal spatial autocorrelation in snail density and infection prevalence was assessed using Moran\u0026rsquo;s I statistic [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. A row-standardized inverse distance weight matrix with a 2 km bandwidth was used based on estimated snail dispersal distances [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Significance was tested using 999 permutations, and Variogram plots were used to characterise spatial dependence.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHotspot analysis\u003c/h3\u003e\n\u003cp\u003eLocal clusters of high snail density (hotspots) and low density (coldspots) were identified using the Getis-Ord Gi* statistic [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Sites with Gi* \u0026gt; 1.96 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were classified as hotspots, and those with Gi* \u0026lt; -1.645 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10) as coldspots.\u003c/p\u003e\n\u003ch3\u003eSpatial interpolation\u003c/h3\u003e\n\u003cp\u003eContinuous surfaces of snail density and key physicochemical parameters were generated using inverse distance weighting (IDW) interpolation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical modeling\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eModel specification\u003c/h2\u003e \u003cp\u003eTwo types of models were developed and compared:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNon-spatial models\u003c/strong\u003e \u003cp\u003eGeneralized linear models (GLMs) with binomial distribution for prevalence (logit link) and negative binomial distribution for density (log link).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSpatial models\u003c/strong\u003e \u003cp\u003eGeneralized linear mixed models (GLMMs) incorporating Gaussian spatial random effects to account for autocorrelation [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePredictor variables\u003c/h2\u003e \u003cp\u003eInitial candidate predictors included: physicochemical parameters (temperature, pH, salinity, TDS, conductivity, DO₂), hydrological factors (water depth, flow rate, water level), habitat characteristics (substrate, vegetation, canopy cover), anthropogenic factors (human activities, domestic/wild animals), geographic variables (altitude, distance to settlement), and season. Continuous variables were standardized (z-scores) for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel selection and validation\u003c/h2\u003e \u003cp\u003eVariable selection was performed using backward elimination based on Akaike Information Criterion (AIC) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Multicollinearity was assessed using variance inflation factors (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. For spatial models, variogram analysis was used to estimate spatial correlation range. Model performance was evaluated using: (1) AIC comparison between spatial and non-spatial models, (2) pseudo-R\u0026sup2; for GLMMs [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], (3) residual spatial autocorrelation testing using Moran\u0026rsquo;s I on residuals, and (4) cross-validation with 70% training and 30% testing data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in R 4.3.1 [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] using packages: \u003cem\u003espatstat\u003c/em\u003e for spatial point pattern analysis [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], \u003cem\u003egstat\u003c/em\u003e for geostatistics [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], \u003cem\u003espaMM\u003c/em\u003e for spatial GLMMs [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], \u003cem\u003espdep\u003c/em\u003e for spatial autocorrelation [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and \u003cem\u003eggplot2\u003c/em\u003e for visualization [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Hotspot mapping and interpolation surfaces were created in QGIS 3.28 [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eThe study protocol was approved by the Gulu University Research Ethics Committee (GUREC-2022-323) and Uganda National Council for Science and Technology (UNCST-HS2571ES). Community consent was obtained through local leaders before sampling. No personal identifying information was collected from individuals using water bodies.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSnail species composition and distribution\u003c/h2\u003e \u003cp\u003eA total of 4,802 freshwater snails belonging to 13 species were collected from 26 sampling sites during both dry and rainy seasons of 2023 (supplementary file 2). The species composition and seasonal distribution are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The three most abundant genera were \u003cem\u003eBiomphalaria\u003c/em\u003e (47.2%, n\u0026thinsp;=\u0026thinsp;2,266), \u003cem\u003eBulinus\u003c/em\u003e (23.7%, n\u0026thinsp;=\u0026thinsp;1,138), and \u003cem\u003eLymnaea\u003c/em\u003e (10.6%, n\u0026thinsp;=\u0026thinsp;510). \u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e was the most abundant species (25.8%, n\u0026thinsp;=\u0026thinsp;1,241), followed by \u003cem\u003eBulinus africanus\u003c/em\u003e (17.7%, n\u0026thinsp;=\u0026thinsp;852) and \u003cem\u003eBiomphalaria sudanica\u003c/em\u003e (15.6%, n\u0026thinsp;=\u0026thinsp;749).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpecies composition and seasonal distribution of freshwater snails in the Lango subregion, northern Uganda (n\u0026thinsp;=\u0026thinsp;4,802)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry season (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRainy season (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% of total\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBulinus africanus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBiomphalaria sudanica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLymnaea natalensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePila ovata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLanistes carinatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBiomphalaria pfeifferi\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBulinus ugandae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMelanoides tuberculata\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBulinus globosus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBivalves\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBulinus forskalii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBulinus nasutus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2223\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2579\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4802\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDetailed site-level data, including coordinates, habitat types, and physicochemical measurements, are available in supplementary file 3 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Seasonal analysis revealed higher overall snail abundance during the rainy season (53.7%) compared to the dry season (46.3%). However, species richness was greater in the dry season (13 species) than the rainy season (11 species). \u003cem\u003eBulinus nasutus\u003c/em\u003e and bivalves were collected exclusively during the dry season. Wilcoxon signed-rank tests confirmed significant seasonal differences in abundance for several species: \u003cem\u003eLymnaea natalensis\u003c/em\u003e was more abundant in the dry season (Z = -3.064, p\u0026thinsp;=\u0026thinsp;0.002), while \u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e (Z = -2.076, p\u0026thinsp;=\u0026thinsp;0.038), \u003cem\u003eB. pfeifferi\u003c/em\u003e (Z = -3.379, p\u0026thinsp;=\u0026thinsp;0.001), and \u003cem\u003ePila ovata\u003c/em\u003e (Z = -2.719, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were more abundant in the rainy season.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSpatial patterns and clustering\u003c/h2\u003e \u003cp\u003eSpatial analysis revealed significant clustering of snail populations across the study area. Global Moran\u0026rsquo;s I statistics indicated positive spatial autocorrelation for total snail density (I\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.008) and for key species: \u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e (I\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.004), \u003cem\u003eB. sudanica\u003c/em\u003e (I\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.018), and \u003cem\u003eBulinus africanus\u003c/em\u003e (I\u0026thinsp;=\u0026thinsp;0.19, p\u0026thinsp;=\u0026thinsp;0.042). This suggests that snail populations are not randomly distributed but clustered in specific areas. Variogram diagnostics supporting spatial dependence are shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEmpirical semivariograms (blue points) and fitted exponential variogram models (orange curves) illustrate spatial autocorrelation in snail metrics across the georeferenced sampling sites. The x-axis displays lag distance (km), and the y-axis shows semivariance. The panels depict: (A) total snails collected (count), (B) Biomphalaria choanomphala density (snails/m\u0026sup2;), (C) Biomphalaria sudanica density (snails/m\u0026sup2;), and (D) infected snails (count) (infection status as detailed in the Methods). Estimated exponential model parameters are: (A) nugget\u0026thinsp;=\u0026thinsp;1.19\u0026times;10\u0026sup3;, partial sill\u0026thinsp;=\u0026thinsp;2.58\u0026times;10⁴, range\u0026thinsp;=\u0026thinsp;9.24 km; (B) nugget\u0026thinsp;=\u0026thinsp;1.23\u0026times;10\u0026sup3;, partial sill\u0026thinsp;=\u0026thinsp;1.00\u0026times;10⁴, range\u0026thinsp;=\u0026thinsp;13.18 km; (C) nugget\u0026thinsp;=\u0026thinsp;2.33\u0026times;10⁻\u0026sup1;\u0026sup1;, partial sill\u0026thinsp;=\u0026thinsp;2.25\u0026times;10\u0026sup3;, range\u0026thinsp;=\u0026thinsp;3.93 km; (D) nugget\u0026thinsp;=\u0026thinsp;2.3, partial sill\u0026thinsp;=\u0026thinsp;0.48, range\u0026thinsp;=\u0026thinsp;6.89 km. The range indicates the approximate distance over which observations remain spatially correlated.\u003c/p\u003e \u003cp\u003eGetis-Ord Gi* hotspot analysis identified three significant clusters of high snail density (hotspots) and two coldspots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The primary hotspot (Cluster A) was located in the Okile swamp and surrounding rice paddies (Z\u0026thinsp;=\u0026thinsp;3.12, p\u0026thinsp;=\u0026thinsp;0.002), covering approximately 4.2 km\u0026sup2;. Secondary hotspots included the Aswa River confluence area (Cluster B: Z\u0026thinsp;=\u0026thinsp;2.89, p\u0026thinsp;=\u0026thinsp;0.004) and Apoka swamp system (Cluster C: Z\u0026thinsp;=\u0026thinsp;2.45, p\u0026thinsp;=\u0026thinsp;0.014). Coldspots were identified in areas with sandy substrates and high human disturbance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMap showing Getis-Ord Gi* statistics for snail density clusters. Red areas indicate significant hotspots (Z\u0026thinsp;\u0026gt;\u0026thinsp;1.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), blue areas indicate coldspots (Z \u0026lt; -1.645, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), and yellow areas indicate non-significant zones. Sampling sites are shown as black dots. Inset shows location of study area within Uganda.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSchistosome infection rates and spatial distribution\u003c/h2\u003e \u003cp\u003eAmong the 3,404 \u003cem\u003eBiomphalaria\u003c/em\u003e and \u003cem\u003eBulinus\u003c/em\u003e snails examined for cercarial shedding, only 5 (0.15%) were infected with human schistosome cercariae. Infection rates by species were: \u003cem\u003eBiomphalaria sudanica\u003c/em\u003e 0.03% (1/749), \u003cem\u003eB. choanomphala\u003c/em\u003e 0.06% (2/1241), and \u003cem\u003eBulinus africanus\u003c/em\u003e 0.06% (2/852). No infections were detected in other snail species.\u003c/p\u003e \u003cp\u003eSpatial analysis of infection indicated one site (Abolet rice field area) where infected snails showed clustering (Z\u0026thinsp;=\u0026thinsp;2.78, p\u0026thinsp;=\u0026thinsp;0.012). Infected snails were collected from three distinct locations: Abolet rice field (Lira District, rainy season), Apoka swamp (Lira District, dry season), and Telela rice field (Lira City, dry season). The spatial distribution of infected snails showed clustering within 1.5 km radius of rice cultivation areas.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePhysicochemical and environmental characteristics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents descriptive statistics for physicochemical parameters measured during the study. Water temperature ranged from 26.3\u0026deg;C to 34.8\u0026deg;C (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD: 29.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36\u0026deg;C), with higher temperatures during the dry season. pH values were generally alkaline (range: 6.4\u0026ndash;9.6, mean: 7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60). Salinity was low overall (0.0\u0026ndash;0.2 g/L, mean: 0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 g/L) but showed seasonal variation. TDS ranged widely from 42 to 906 ppm (mean: 171.46\u0026thinsp;\u0026plusmn;\u0026thinsp;158.27 ppm), with higher values in the rainy season likely due to increased runoff. Dissolved oxygen showed considerable variation (1.6\u0026ndash;50.3 mg/L, mean: 19.98\u0026thinsp;\u0026plusmn;\u0026thinsp;14.39 mg/L), with higher values in the dry season.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of physicochemical parameters by season\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry season Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRainy season Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOverall Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (Range)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.05\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12 (26.3\u0026ndash;33.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e30.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.20 (27.5\u0026ndash;34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e29.98\u0026thinsp;\u0026plusmn;\u0026thinsp;2.36 (26.3\u0026ndash;34.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 (6.4\u0026ndash;8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57 (7.1\u0026ndash;9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 (6.4\u0026ndash;9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalinity (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 (0.0\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 (0.0\u0026ndash;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04 (0.0\u0026ndash;0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDS (ppm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e151.54\u0026thinsp;\u0026plusmn;\u0026thinsp;125.33 (42\u0026ndash;580)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e191.38\u0026thinsp;\u0026plusmn;\u0026thinsp;183.56 (58\u0026ndash;906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e171.46\u0026thinsp;\u0026plusmn;\u0026thinsp;158.27 (42\u0026ndash;906)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConductivity (mS/cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20 (0.01\u0026ndash;0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35 (0.04\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 (0.01\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDO₂ (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.31\u0026thinsp;\u0026plusmn;\u0026thinsp;12.45 (12.5\u0026ndash;50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.64\u0026thinsp;\u0026plusmn;\u0026thinsp;5.12 (1.6\u0026ndash;22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e19.98\u0026thinsp;\u0026plusmn;\u0026thinsp;14.39 (1.6\u0026ndash;50.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHabitat characteristics varied across sites: 80.8% had muddy substrates, 53.8% had water depths of 0.5\u0026ndash;1 m, and 40.4% had flow rates of 0.5\u0026ndash;1 m/s. Domestic animals (primarily cattle) were present at 96.2% of sites, while water birds were observed at 67.3% of sites. Anthropogenic activities were common, with washing/bathing occurring at 65.4% of sites and rice cultivation at 30.8% of sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of snail prevalence: Spatial vs. non-spatial models\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents results from logistic regression models predicting snail prevalence. For \u003cem\u003eBiomphalaria sudanica\u003c/em\u003e, the spatial GLMM explained 73.1% of variation (pseudo-R\u0026sup2; = 0.731), significantly outperforming the non-spatial GLM (ΔAIC\u0026thinsp;=\u0026thinsp;15.3). Key predictors included salinity (OR\u0026thinsp;=\u0026thinsp;0.21, 95% CI: 0.09\u0026ndash;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TDS (OR\u0026thinsp;=\u0026thinsp;0.87, 95% CI: 0.79\u0026ndash;0.96, p\u0026thinsp;=\u0026thinsp;0.005), conductivity (OR\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.14\u0026ndash;0.69, p\u0026thinsp;=\u0026thinsp;0.004), and water depth (0.5\u0026ndash;1 m vs. \u0026lt;0.5 m: OR\u0026thinsp;=\u0026thinsp;0.42, 95% CI: 0.21\u0026ndash;0.84, p\u0026thinsp;=\u0026thinsp;0.014). The spatial random effect variance was 0.86, accounting for 22% of total variance.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e, the spatial model explained 82.2% of variation (pseudo-R\u0026sup2; = 0.822, ΔAIC\u0026thinsp;=\u0026thinsp;12.7). Significant predictors included dissolved oxygen (OR\u0026thinsp;=\u0026thinsp;1.18, 95% CI: 1.05\u0026ndash;1.33, p\u0026thinsp;=\u0026thinsp;0.006), presence in ponds (OR\u0026thinsp;=\u0026thinsp;4.56, 95% CI: 1.89\u0026ndash;11.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), rice paddies (OR\u0026thinsp;=\u0026thinsp;3.78, 95% CI: 1.42\u0026ndash;10.07, p\u0026thinsp;=\u0026thinsp;0.008), and washing/bathing activities (OR\u0026thinsp;=\u0026thinsp;2.34, 95% CI: 1.28\u0026ndash;4.28, p\u0026thinsp;=\u0026thinsp;0.006). Car washing showed a negative association (OR\u0026thinsp;=\u0026thinsp;0.41, 95% CI: 0.19\u0026ndash;0.88, p\u0026thinsp;=\u0026thinsp;0.022).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression models for snail prevalence: comparison of spatial GLMMs and non-spatial GLMs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies \u0026amp; Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePseudo-R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. sudanica\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(GLM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35 (0.17\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e127.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90 (0.83\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. sudanica\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(GLMM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.21 (0.09\u0026ndash;0.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e112.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87 (0.79\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.14\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater depth (0.5\u0026ndash;1m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42 (0.21\u0026ndash;0.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSpatial variance (σ\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. choanomphala\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(GLM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12 (1.01\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePond habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.89 (1.65\u0026ndash;9.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. choanomphala\u003c/em\u003e\u003c/p\u003e \u003cp\u003e(GLMM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.05\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePond habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.56 (1.89\u0026ndash;11.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRice paddy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.78 (1.42\u0026ndash;10.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWashing/bathing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.34 (1.28\u0026ndash;4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCar washing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41 (0.19\u0026ndash;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSpatial variance (σ\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003ePredictors of snail density: Spatial vs. non-spatial models\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents negative binomial regression results for snail density. For \u003cem\u003eBiomphalaria sudanica\u003c/em\u003e, the spatial model (ΔAIC\u0026thinsp;=\u0026thinsp;14.2) identified positive associations with water temperature (IRR\u0026thinsp;=\u0026thinsp;1.41, 95% CI: 1.22\u0026ndash;1.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), pH (IRR\u0026thinsp;=\u0026thinsp;2.72, 95% CI: 1.76\u0026ndash;4.20, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TDS (IRR\u0026thinsp;=\u0026thinsp;1.01, 95% CI: 1.00\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.002), and washing activities (IRR\u0026thinsp;=\u0026thinsp;4.62, 95% CI: 2.14\u0026ndash;9.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Negative associations were found with dissolved oxygen (IRR\u0026thinsp;=\u0026thinsp;0.94, 95% CI: 0.90\u0026ndash;0.98, p\u0026thinsp;=\u0026thinsp;0.003) and dry season (IRR\u0026thinsp;=\u0026thinsp;0.19, 95% CI: 0.09\u0026ndash;0.40, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e, density increased with dissolved oxygen (IRR\u0026thinsp;=\u0026thinsp;1.07, 95% CI: 1.03\u0026ndash;1.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and presence in streams (IRR\u0026thinsp;=\u0026thinsp;5.07, 95% CI: 2.44\u0026ndash;10.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but decreased with temperature (IRR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.38\u0026ndash;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TDS (IRR\u0026thinsp;=\u0026thinsp;0.96, 95% CI: 0.94\u0026ndash;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and conductivity (IRR\u0026thinsp;=\u0026thinsp;0.28, 95% CI: 0.15\u0026ndash;0.52, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNegative binomial regression models for snail density: comparison of spatial GLMMs and non-spatial GLMs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies \u0026amp; Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncidence Rate Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. sudanica\u003c/em\u003e\u0026nbsp;(GLM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.15\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e345.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.45 (1.62\u0026ndash;3.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWashing activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.85 (1.85\u0026ndash;8.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. sudanica\u003c/em\u003e\u0026nbsp;(GLMM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41 (1.22\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e331.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.72 (1.76\u0026ndash;4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.90\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWashing activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.62 (2.14\u0026ndash;9.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry season\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.19 (0.09\u0026ndash;0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSpatial variance (σ\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. choanomphala\u003c/em\u003e\u0026nbsp;(GLM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05 (1.01\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e312.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.12 (2.01\u0026ndash;8.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eB. choanomphala\u003c/em\u003e\u0026nbsp;(GLMM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDO₂\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e299.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream habitat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.07 (2.44\u0026ndash;10.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50 (0.38\u0026ndash;0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.94\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.28 (0.15\u0026ndash;0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSpatial variance (σ\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSpatial interpolation of key parameters\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents IDW interpolation surfaces for \u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e density and key environmental predictors. High-density areas corresponded with regions of moderate TDS (150\u0026ndash;250 ppm), neutral to slightly alkaline pH (7.5\u0026ndash;8.0), and proximity to human settlements. The interpolation revealed a gradient of decreasing snail density from central wetland areas toward peripheral regions with sandy substrates and lower vegetation cover.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFour-panel figure showing inverse distance weighting interpolation surfaces for: (A) Biomphalaria choanomphala density (snails/m\u0026sup2;), (B) Total dissolved solids (ppm), (C) pH, and (D) Distance to nearest settlement (km). Warmer colors indicate higher values. Cross symbols show sampling locations.\u003c/em\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eModel validation and performance\u003c/h2\u003e \u003cp\u003eSpatial models consistently outperformed non-spatial counterparts across all response variables (mean ΔAIC\u0026thinsp;=\u0026thinsp;13.4, range: 8.3\u0026ndash;18.1). Residual spatial autocorrelation was significantly reduced in spatial models (Moran\u0026rsquo;s I on residuals: -0.03 to 0.11, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) compared to non-spatial models (Moran\u0026rsquo;s I: 0.24\u0026ndash;0.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Cross-validation showed better predictive accuracy for spatial models (mean absolute error reduction: 18\u0026ndash;32% across models).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study represents one of the first comprehensive integrations of geostatistical analysis with ecological modeling of freshwater snail intermediate hosts in northern Uganda. Our findings demonstrate significant spatial structuring of snail populations, with clear hotspots of high density and sites where infection was detected that traditional non-spatial approaches would miss. The spatial GLMMs consistently outperformed their non-spatial counterparts, explaining 18\u0026ndash;24% more variance by incorporating spatial random effects.\u003c/p\u003e \u003cp\u003eThe overall infection rate (0.15%) is lower than rates reported from the Lake Victoria basin (0.5\u0026ndash;2.8%) [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], but it aligns with findings from other inland regions of Uganda [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. This difference may arise from variations in transmission intensity, human water contact practices, or snail compatibility with local schistosome strains [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The clustering of infected snails in rice paddies near settlements emphasizes the importance of agricultural water management in creating potential transmission sites, consistent with studies from Tanzania and Kenya [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe identified hotspots in Okile swamp, Aswa river confluence, and Apoka swamp exhibit common features: moderate TDS (150\u0026ndash;250 ppm), neutral to slightly alkaline pH (7.5\u0026ndash;8.0), muddy substrates, and proximity to human settlements with frequent water contact activities. These conditions match the known ecological preferences of \u003cem\u003eBiomphalaria\u003c/em\u003e and \u003cem\u003eBulinus\u003c/em\u003e species [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. The negative correlation between car washing and snail prevalence/density suggests that chemical pollutants or physical disturbance from this activity may harm snail habitats, a finding supported by studies in Nigeria and South Africa [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeasonal variations in snail abundance followed expected patterns, with higher densities during the rainy season due to expanded habitats and increased nutrient inputs [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. However, the greater species richness during the dry season may reflect concentration effects as water bodies contract, making snails easier to sample. This has implications for control timing, suggesting that dry-season interventions may be more effective for reducing snail populations.\u003c/p\u003e \u003cp\u003eThe spatial autocorrelation detected for key species (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.19\u0026ndash;0.32) indicates that snail populations are not randomly distributed but exhibit patchiness at scales of 1\u0026ndash;3 km. This clustering likely results from both environmental heterogeneity (habitat suitability gradients) and limited dispersal capabilities of snails [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Similar spatial patterns have been reported for snail populations in Kenya and Tanzania [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAnthropogenic activities exhibited complex spatial relationships with snail distributions. Washing and bathing activities generally increased snail density, possibly due to nutrient enrichment from soap and organic matter [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Conversely, car washing and swimming/playing were linked to adverse effects, likely owing to habitat disturbance and chemical inputs [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. These findings highlight the importance of context-specific interventions that consider local water-use practices.\u003c/p\u003e \u003cp\u003eThe superior performance of spatial models (ΔAIC\u0026thinsp;=\u0026thinsp;8.3\u0026ndash;18.1) highlights the importance of considering spatial autocorrelation in ecological studies of snail distributions. Ignoring spatial dependencies can result in biased parameter estimates, increased Type I errors, and poor predictions for targeted control [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Our spatial GLMMs effectively captured both fixed effects of environmental predictors and random spatial effects, offering more reliable inference.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged when interpreting our findings. First, the cross-sectional design offers only a snapshot of snail distributions and infection rates. Long-term sampling over multiple years would better capture temporal dynamics and seasonal changes. Second, although we included a comprehensive range of environmental predictors, unmeasured factors such as predation pressure, competition, and microhabitat characteristics could influence snail distributions. Third, the moderate spatial resolution (26 sites across approximately 2,000 km\u0026sup2;) may not capture fine-scale heterogeneity, especially in complex wetland systems. However, our stratified random sampling was explicitly designed to capture major habitat types across the study area, and the hotspot analysis successfully identified significant risk clusters despite this potential limitation. Fourth, cercarial shedding has limited sensitivity for detecting prepatent infections, which might lead to an underestimation of true infection rates. Additionally, infection detection relied on cercarial shedding without molecular confirmation, which may underestimate true infection rates and limit species-specific identification of schistosomes. Molecular methods would enable more accurate detection of infections [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Finally, our study area reflects the specific ecological conditions of the Lango subregion; therefore, findings might not be directly generalizable to regions with different hydrological or land-use features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eImplications for control\u003c/h2\u003e \u003cp\u003eThe spatial hotspots identified in this study serve as potential priority targets for snail control measures. Combining approaches such as environmental modification (habitat disturbance in hotspot areas), chemical control (targeted mollusciciding), biological control (duck introduction in rice paddies), and community-based sanitation improvements could be considered in these high-risk zones [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. The spatial risk maps produced can assist district health authorities in planning targeted mass drug administration and health education campaigns [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFuture research should focus on: (1) longitudinal monitoring of identified hotspots to understand transmission dynamics, (2) experimental manipulation of key environmental factors to test causality, (3) integration of remote sensing data for larger-scale habitat mapping, and (4) community participatory approaches to develop context-appropriate interventions [\u003cspan additionalcitationids=\"CR94\" citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study highlights the significant value of geostatistical approaches in understanding the spatial ecology of schistosomiasis intermediate host snails in northern Uganda. The use of spatial clustering analysis, hotspot mapping, and spatial regression modelling identified distinct, high-risk snail density clusters mainly located in rice paddies and swamps near human settlements, which would be overlooked by traditional non-spatial methods. The demonstrated effectiveness of spatial models, combined with key environmental and anthropogenic predictors, provides a strong evidence base for guiding consideration of targeted control measures. Our findings support the consideration of spatially explicit interventions, such as targeted snail control in high-density areas, community-led habitat modification, and the integration of risk maps into health planning, which could improve the efficiency of schistosomiasis control efforts in Uganda and similar endemic areas.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAIC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAkaike Information Criterion\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDO₂\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDissolved Oxygen\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Linear Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneralized Linear Mixed Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGPS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlobal Positioning System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIDW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse Distance Weighting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIncidence Rate Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTotal Dissolved Solids\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVariance Inflation Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was approved by the Gulu University Research Ethics Committee (reference: GUREC-2022-323) and the Uganda National Council for Science and Technology (reference: UNCST-HS2571ES). Permission was obtained from district and local authorities before commencing the study. Community consent was obtained through meetings with local leaders and community representatives. Individual consent was not required as the study did not involve human subjects directly, only environmental sampling and snail collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted as part of academic research requirements at Gulu University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;JPB: Conceptualization, study design, fieldwork, data collection, laboratory analysis, statistical analysis, spatial analysis, manuscript writing, revision. RO: Methodology, supervision, validation, manuscript review. MN: Fieldwork, data collection, supervision, laboratory analysis, data curation. HA: Methodology, manuscript review. GMM: Statistical analysis, methodology, validation. RE: Methodology, manuscript review. EIOA: Supervision, conceptualization, validation, manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;We thank the District Health Officers of Lira and Kole districts for facilitating the study. We acknowledge the technical support provided by the Vector Control Division, Ministry of Health Uganda. We are grateful to the village health teams and community members who assisted with fieldwork and site access. We thank the laboratory technicians at Gulu University for assistance with snail processing. Finally, we acknowledge the Uganda National Meteorological Authority for providing climate data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All data generated or analysed during this study are included in this published article and its supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrown DS. Freshwater snails of Africa and their medical importance. 2nd ed. London: Taylor \u0026amp; Francis; 1994.\u003c/li\u003e\n \u003cli\u003eColley DG, Bustinduy AL, Secor WE, King CH. Human schistosomiasis. 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Evaluating the ecology and distribution of snail hosts of Schistosoma at the water bodies in Ihitte-Uboma area of Imo State. Adv Res. 2020;21(8):52\u0026ndash;64.\u003c/li\u003e\n \u003cli\u003eMcCreesh N, Booth M. Challenges in predicting the effects of climate change on Schistosoma mansoni and Schistosoma haematobium transmission potential. Trends Parasitol. 2013;29(11):548\u0026ndash;55.\u003c/li\u003e\n \u003cli\u003eKalinda C, Chimbari M, Mukaratirwa S. Effect of temperature on the Bulinus globosus \u0026mdash; Schistosoma haematobium system. Infect Dis Poverty. 2017; 6:57.\u003c/li\u003e\n \u003cli\u003eStensgaard AS, Utzinger J, Vounatsou P, H\u0026uuml;rlimann E, Schur N, Saarnak CF, et al. Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa: does climate matter? Acta Trop. 2013;128(2):378\u0026ndash;90.\u003c/li\u003e\n \u003cli\u003ePedersen UB, Stendel M, Midzi N, Mduluza T, Soko W, Stensgaard AS, et al. Modelling climate change impact on the spatial distribution of fresh water snails hosting trematodes in Zimbabwe. Parasit Vectors. 2014; 7:536.\u003c/li\u003e\n \u003cli\u003eHamburger J, Hoffman O, Kariuki HC, Muchiri EM, Ouma JH, Koech DK, et al. Large-scale, polymerase chain reaction-based surveillance of Schistosoma haematobium DNA in snails from transmission sites in coastal Kenya: a new tool for studying the dynamics of snail infection. Am J Trop Med Hyg. 2004;71(6):765\u0026ndash;73.\u003c/li\u003e\n \u003cli\u003ePennance T, Person B, Muhsin MA, Khamis AN, Muhsin J, Khamis IS, et al. Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots. Parasit Vectors. 2016; 9:646.\u003c/li\u003e\n \u003cli\u003eGrimes JET, Croll D, Harrison WE, Utzinger J, Freeman MC, Templeton MR. The roles of water, sanitation and hygiene in reducing schistosomiasis: a review. Parasit Vectors. 2015; 8:156.\u003c/li\u003e\n \u003cli\u003eFreeman MC, Ogden S, Jacobson J, Abbott D, Addiss DG, Amynie AG, et al. Integration of water, sanitation, and hygiene for the prevention and control of neglected tropical diseases: a rationale for inter-sectoral collaboration. PLoS Negl Trop Dis. 2013;7(9): e2439.\u003c/li\u003e\n \u003cli\u003eArcher J, O\u0026rsquo;Halloran L, Al-Shehri H, Summers S, Bhattacharyya T, Kabatereine NB, et al. A scoping review of the role of environmental factors on the transmission of schistosomiasis in sub-Saharan Africa. PLoS Negl Trop Dis. 2023;17(4): e0011182.\u003c/li\u003e\n \u003cli\u003eDormann CF. Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Glob Ecol Biogeogr. 2007;16(2):129\u0026ndash;38.\u003c/li\u003e\n \u003cli\u003eMiller JA. Species distribution modeling. Geogr Compass. 2010;4(6):490\u0026ndash;509.\u003c/li\u003e\n \u003cli\u003eHamburger J, Xu Y, Ramzy RM, Jourdane J, Ruppel A. Development and laboratory evaluation of a polymerase chain reaction for monitoring Schistosoma mansoni infestation of water. Am J Trop Med Hyg. 1998;59(3):468\u0026ndash;73.\u003c/li\u003e\n \u003cli\u003eGandasegui J, Fern\u0026aacute;ndez-Soto P, Hern\u0026aacute;ndez-Goenaga J, L\u0026oacute;pez-Ab\u0026aacute;n J, Vicente B, Muro A. Biompha-LAMP: A new rapid loop-mediated isothermal amplification assay for detecting Schistosoma mansoni in Biomphalaria glabrata snail host. PLoS Negl Trop Dis. 2016;10(12): e0005225.\u003c/li\u003e\n \u003cli\u003eSokolow SH, Wood CL, Jones IJ, Swartz SJ, Lopez M, Hsieh MH, et al. Global assessment of schistosomiasis control over the past century shows targeting the snail intermediate host works best. PLoS Negl Trop Dis. 2016;10(7): e0004794.\u003c/li\u003e\n \u003cli\u003eKing CH, Sutherland LJ, Bertsch D. Systematic review and meta-analysis of the impact of chemical-based mollusciciding for control of Schistosoma mansoni and S. haematobium transmission. PLoS Negl Trop Dis. 2015;9(12): e0004290.\u003c/li\u003e\n \u003cli\u003eCampbell SJ, Savage GB, Gray DJ, Atkinson JA, Soares Magalh\u0026atilde;es RJ, Nery SV, et al. Water, sanitation, and hygiene (WASH): a critical component for sustainable soil-transmitted helminth and schistosomiasis control. PLoS Negl Trop Dis. 2014;8(4): e2651.\u003c/li\u003e\n \u003cli\u003eCampbell CH, Biritwum NK, Woods G, Velleman Y, Fleming F, Stothard JR. Tailoring water, sanitation, and hygiene (WASH) targets for soil-transmitted helminthiasis and schistosomiasis control. Trends Parasitol. 2018;34(1):53\u0026ndash;63.\u003c/li\u003e\n \u003cli\u003eKnopp S, Ame SM, Hattendorf J, Ali SM, Khamis IS, Bakar F, et al. Urogenital schistosomiasis elimination in Zanzibar: accuracy of urine filtration and haematuria reagent strips for diagnosing light intensity Schistosoma haematobium infections. Parasit Vectors. 2018; 11:552.\u003c/li\u003e\n \u003cli\u003eGarchitorena A, Sokolow SH, Roche B, Ngonghala CN, Jocque M, Lund A, et al. Disease ecology, health and the environment: a framework to account for ecological and socio-economic drivers in the control of neglected tropical diseases. Philos Trans R Soc Lond B Biol Sci. 2017;372(1722):20160128.\u003c/li\u003e\n \u003cli\u003eB\u0026auml;renbold O, Garba A, Colley DG, Fleming FM, Haggag AA, Ramzy RMR, et al. Translating preventive chemotherapy prevalence thresholds for Schistosoma mansoni from the Kato-Katz technique into the point-of-care circulating cathodic antigen diagnostic test. PLoS Negl Trop Dis. 2018;12(12): e0006941.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biomphalaria, Bulinus, Freshwater snails, Geostatistics, Hotspot mapping, Lango subregion, Schistosomiasis, Spatial analysis, Uganda","lastPublishedDoi":"10.21203/rs.3.rs-8643236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8643236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFreshwater snails of the genera \u003cem\u003eBiomphalaria\u003c/em\u003e, \u003cem\u003eBulinus\u003c/em\u003e, and \u003cem\u003eLymnaea\u003c/em\u003e serve as intermediate hosts for trematodes causing schistosomiasis and fascioliasis, diseases of major public health concern in sub-Saharan Africa. In Uganda's Lango subregion, schistosomiasis remains endemic despite control efforts, yet comprehensive spatial and ecological analyses of snail intermediate host distributions are lacking. This study employed geostatistical approaches to identify high-risk snail habitats and sites where schistosome cercarial shedding was detected to inform targeted control strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA cross-sectional study was conducted during the dry and rainy seasons of 2023 across 26 georeferenced sites in Lira and Kole districts. Freshwater snails were collected using standardized methods and identified morphologically. Cercarial shedding tests determined infection status but were limited to detecting patent infections. Physicochemical parameters (pH, salinity, total dissolved solids, dissolved oxygen, temperature, and conductivity) and ecological variables were measured. Spatial analysis included Moran\u0026rsquo;s I for autocorrelation, Getis-Ord Gi* for hotspot detection, and inverse distance weighting for interpolation. Generalized linear mixed models with spatial random effects were used to assess predictors of snail prevalence and density, compared with non-spatial models using the Akaike Information Criterion.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 4,802 snails from 13 species were collected, with \u003cem\u003eBiomphalaria choanomphala\u003c/em\u003e (25.8%) being most abundant. Significant spatial clustering was detected for \u003cem\u003eB. choanomphala\u003c/em\u003e (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.004) and \u003cem\u003eB. sudanica\u003c/em\u003e (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.24, p\u0026thinsp;=\u0026thinsp;0.018). Three density hotspots and one site where infected snails clustered were identified, primarily in rice paddies and swamps near human settlements. Overall infection rate was 0.15% (5/3404 tested snails), with \u003cem\u003eB. choanomphala\u003c/em\u003e showing the highest infection 0.06% (2/1241). Spatial GLMMs outperformed non-spatial models (ΔAIC\u0026thinsp;=\u0026thinsp;12.7\u0026ndash;15.3), revealing significant effects of salinity (odds ratio\u0026thinsp;=\u0026thinsp;0.21, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), total dissolved solids (β = -0.03, p\u0026thinsp;=\u0026thinsp;0.002), dissolved oxygen (β\u0026thinsp;=\u0026thinsp;0.54, p\u0026thinsp;=\u0026thinsp;0.003), and anthropogenic activities. Spatial random effects accounted for 18\u0026ndash;24% of residual variation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study demonstrates the significant added value of geostatistical methods in identifying snail intermediate host clusters and sites with detected infections. The integration of spatial analysis with ecological modeling provides a robust framework for potential targeted snail control. Our findings suggest that focused interventions in identified high-density areas, integration of spatial risk maps into district health planning, and community engagement in modifying high-risk water contact sites could help reduce schistosomiasis transmission in the Lango subregion.\u003c/p\u003e","manuscriptTitle":"Spatio-ecological determinants of freshwater snail intermediate hosts and schistosome infection in the Lango subregion, Northern Uganda: a geostatistical approach to targeted disease control","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 15:57:17","doi":"10.21203/rs.3.rs-8643236/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-29T19:22:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31134863536677245111761195148568429252","date":"2026-03-20T22:06:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314821465592768035506795073952204603636","date":"2026-03-04T13:24:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-02T12:42:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T14:54:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasites \u0026 Vectors","date":"2026-01-31T08:25:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69a89d08-c7dc-4eef-980a-043d8c87ede0","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-05T15:57:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 15:57:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8643236","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8643236","identity":"rs-8643236","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}