A geospatial analysis of local intermediate snail host distributions provides insight into schistosomiasis risk within under-sampled areas of southern Lake Malawi.

BACKGROUND: Along the southern shoreline of Lake Malawi, the incidence of schistosomiasis is increasing with snails of the genera Bulinus and Biomphalaria transmitting urogenital and intestinal schistosomiasis, respectively. Since the underlying distribution of snails is partially known, often being focal, developing pragmatic spatial models that interpolate snail information across under-sampled regions is required to understand and assess current and future risk of schistosomiasis. METHODS: A secondary geospatial analysis of recently collected malacological and environmental survey data was undertaken. Using a Bayesian Poisson latent Gaussian process model, abundance data were fitted for Bulinus and Biomphalaria. Interpolating the abundance of snails along the shoreline (given their relative distance along the shoreline) was achieved by smoothing, using extracted environmental rainfall, land surface temperature (LST), evapotranspiration, normalised difference vegetation index (NDVI) and soil type covariate data for all predicted locations. Our adopted model used a combination of two-dimensional (2D) and one dimensional (1D) mapping. RESULTS: A significant association between normalised difference vegetation index (NDVI) and abundance of Bulinus spp. was detected (log risk ratio - 0.83, 95% CrI - 1.57, - 0.09). A qualitatively similar association was found between NDVI and Biomphalaria sp. but was not statistically significant (log risk ratio - 1.42, 95% CrI - 3.09, 0.10). Analyses of all other environmental data were considered non-significant. CONCLUSIONS: The spatial range in which interpolation of snail distributions is possible appears < 10km owing to fine-scale biotic and abiotic heterogeneities. The forthcoming challenge is to refine geospatial sampling frameworks with future opportunities to map schistosomiasis within actual or predicted snail distributions. In so doing, this would better reveal local environmental transmission possibilities.

Modelling the age-prevalence relationship in schistosomiasis: A secondary data analysis of school-aged-children in Mangochi District, Lake Malawi.

Schistosomiasis is an aquatic snail borne parasitic disease, with intestinal schistosomiasis (IS) and urogenital schistosomiasis (UGS) caused by Schistosoma mansoni and S. haematobium infections, respectively. School-aged-children (SAC) are a known vulnerable group and can also suffer from co-infections. Along the shoreline of Lake Malawi a newly emerging outbreak of IS is occurring with increasing UGS co-infection rates. Age-prevalence (co)infection profiles are not fully understood. To shed light on these (co)infection trends by Schistosoma species and by age of child, we conducted a secondary data analysis of primary epidemiological data collected from SAC in Mangochi District, Lake Malawi, as published previously. Available diagnostic data by child, were converted into binary response infection profiles for 520 children, aged 6-15, across 12 sampled schools. Generalised additive models were then fitted to mono- and dual-infections. These were used to identify consistent population trends, finding the prevalence of IS significantly increased [p = 8.45e-4] up to 11 years of age then decreasing thereafter. A similar age-prevalence association was observed for co-infection [p = 7.81e-3]. By contrast, no clear age-infection pattern for UGS was found [p = 0.114]. Peak prevalence of Schistosoma infection typically occurs around adolescence; however, in this newly established IS outbreak with rising prevalence of UGS co-infections, the peak appears to occur earlier, around the age of 11 years. As the outbreak of IS fulminates, further temporal analysis of the age-relationship with Schistosoma infection is justified. This should refer to age-prevalence models which could better reveal newly emerging transmission trends and Schistosoma species dynamics. Dynamical modelling of infections, alongside malacological niche mapping, should be considered to guide future primary data collection and intervention programmes.