Modeling freshwater snail habitat suitability and areas of potential snail-borne disease transmission in Uganda

Abstract

Abstract. Geographic information system (GIS)-based modeling of an intermediate host snail species' environmental requirements using known occurrence records can provide estimates of its spatial distribution. When other data are lacking, this can be used as a rough spatial prediction of potential snail-borne disease transmission areas. Furthermore, knowledge of abiotic factors affecting intra-molluscan parasitic development can be used to make "masks" based on remotely sensed climatic data, and these can in turn be used to refine these predictions. We used data from a recent freshwater snail survey from Uganda, environmental data and the genetic algorithm for rule-set prediction (GARP) to map the potential distribution of snail species known to act as intermediate hosts of several human and animal parasites. The results suggest that large areas of Uganda are suitable habitats for many of these snail species, indicating a large potential for disease transmission. The lack of parasitological data still makes it difficult to determine the magnitude of actual disease transmission, but the predicted snail distributions might be used as indicators of potential present and future risk areas. Some of the predicted snail distribution maps were furthermore combined with temperature masks delineating suitable temperature regimes of the parasites they host. This revealed the coinciding suitable areas for snail and parasite, but also areas suitable for host snails, but apparently not for the parasites. Assuming that the developed models correctly reflect areas suitable for transmission, the applied approach could prove useful for targeting control interventions.

Modelling spatial distribution of snails transmitting parasitic worms with importance to human and animal health and analysis of distributional changes in relation to climate

“…Extensive research in snail ecology and results from numerous modelling studies, describe which candidate variables to use (Kristensen et al, 2001;Malone et al, 2001;Stensgaard et al, 2006Stensgaard et al, , 2013Zhou et al, 2008;Stensgaard, 2011). For the purpose of comparing two different time periods, attention was paid to identifying and compiling environmental datasets with a quality that justifies temporal comparison.…”

Predicting the spatial distribution of Biomphalaria straminea, a potential intermediate host for Schistosoma mansoni, in China

“…A possible alternative way to overcome these challenges is to determine factors that regulate the snail abundance, and use this information to predict its presence in un-surveyed areas (Kristensen et al, 2001;Stensgaard et al, 2006). Suitable and better models for the distribution and transmission of schistosomiasis can be obtained by integrating the essential biology of the parasite, the intermediate snail host and the definitive human host .…”

“…Different spatial modelling techniques have been used to model Biomphalaria distribution such as maximum entropy (MaxEnt) (Scholte et al, 2012;Stensgaard et al, 2013;Pedersen et al, 2014), genetic algorithm for rule-set prediction (GARP) (Stensgaard et al, 2006), and geostatic indicator Kriging (Guimarães et al, 2009). There are also non-regression models such as the Bayesian geostatistical approach for modelling Biomphalaria spp., distribution (Raso et al, 2005;Vounatsou et al, 2009;Standley et al, 2012;Schur et al, 2013).…”

Bayesian geostatistical modelling for mapping schistosomiasis transmission

“…To our knowledge, anisotropy has not been considered in any of the schistosomiasis transmission mapping exercises carried out thus far. We speculate that the spatial correlation is stronger on directions towards transmission sites rather than in the opposite direction, governed by hydrological factors upon which the intermediate host snails depend (Kitron et al 2006 ;Stensgaard et al 2006 ;Clennon et al 2007).…”

“…The data are spatially correlated because common exposures influence transmission similarly at neighbouring locations. Among other factors, these common exposures include climatic and environmental features governing the survival and longevity of the intermediate host snails (Stensgaard et al 2006) and proximity of human habitations to transmission sites (Booth et al 2004 ;Kitron et al 2006). Risk maps of schistosomiasis are produced by predicting the transmission outcome at non-sampled locations.…”