Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa: does climate matter?

Stensgaard, Anna‐Sofie; Utzinger, Jürg; Vounatsou, Penelope; Hürlimann, Eveline; Schur, Nadine; Saarnak, Christopher F.L.; Simoonga, Christopher; Mubita, Patricia; Kabatereine, Narcis B.; Tchuenté, Louis-Albert Tchuem; Rahbek, Carsten; Kristensen, Troels

doi:10.1016/j.actatropica.2011.11.010

Cited by 143 publications

(141 citation statements)

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“…Clearly, climate influences the distribution of both vectors and pathogens, as we found here, and as shown in many other studies (e.g., Mordecai et al 2013, Stensgaard et al 2013). Yet, if climate were the sole influence on the biogeographic regions of vectored pathogens, we would expect those regions to simply reflect the climatic zones of the world.…”

Section: Discussionsupporting

confidence: 70%

“…Others have suggested that diversity and prevalence of human pathogen species affect human politics, the likelihood of war ), and religion (Fincher and Thornhill 2008) among other aspects of socio-politics (Nettle 2009), such that the biogeographic distribution of pathogens has the potential to pervasively affect human life and societies. If the limits of pathogen distributions are determined not only by climate and attempts at disease control but also the ancient biogeographic distribution of vectors and hosts (e.g., Stensgaard et al 2013), the differences among regions in their pathogens (and consequently religions, behaviors, and socio-politics) may be relatively persistent features of those regions.…”

Section: Introductionmentioning

confidence: 99%

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Global biogeographic regions in a human‐dominated world: the case of human diseases

et al. 2014

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. 2014. Global biogeographic regions in a human-dominated world: the case of human diseases.Ecosphere 5(11):143. http://dx.doi.org/10.1890/ES14-00201.1Abstract. Since the work of Alfred Russel Wallace, biologists have sought to divide the world into biogeographic regions that reflect the history of continents and evolution. These divisions not only guide conservation efforts, but are also the fundamental reference point for understanding the distribution of life. However, the biogeography of human-associated species-such as pathogens, crops, or even house guests-has been largely ignored or discounted. As pathogens have the potential for direct consequences on the lives of humans, domestic animals, and wildlife it is prudent to examine their potential biogeographic history. Furthermore, if distinct regions exist for human-associated pathogens, it would provide possible connections between human wellbeing and pathogen distributions, and, more generally, humans and the deep evolutionary history of the natural world. We tested for the presence of biogeographic regions for diseases of humans due to pathogens using country-level disease composition data and compared the regions for vectored and non-vectored diseases. We found discrete biogeographic regions for diseases, with a stronger influence of biogeography on vectored than non-vectored diseases. We also found significant correlations between these biogeographic regions and environmental or sociopolitical factors. While some biogeographic regions reflected those already documented for birds or mammals, others reflected colonial history. From the perspective of diseases caused by pathogens, humans have altered but not evaded the influence of ancient biogeography. This work is the necessary first step in examining the biogeographic relationship between humans and their associates.

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Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Global biogeographic regions in a human‐dominated world: the case of human diseases

et al. 2014

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“…In 2011, the open-access GNTD database containing georeferenced schistosomiasis prevalence data was launched to fill this gap. This database allows extraction of compiled prevalence data from various published and unpublished sources for many different countries , which stimulated spatially explicit analyses, such as risk mapping, studies of the distribution of intermediate host snails and estimates of treatment needs for the school-aged population (Schur et al, , 2012Stensgaard et al, 2013). Employing this rich source of schistosomiasis survey data, the geographical distributions of the three human Schistosoma species that occur in Nigeria have been mapped.…”

Section: Discussionmentioning

confidence: 99%

“…For a detailed description of the various data sources, the data extraction process, the database system and internal quality checks, the reader is invited to consult recently published papers, e.g. Hürlimann et al (2011 and Stensgaard et al (2013). Studies lacking information about the geographical coordinates of survey locations were retrospectively geo-referenced using a variety of sources, such as GEOnet Names Servers (http://earthinfo.nga.mil/gns/html/index.html) and Google maps (http://maps.google.com).…”

Section: Geo-referencing Of Identified Survey Datamentioning

confidence: 99%

Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

et al. 2013

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Abstract. Schistosomiasis prevalence data for Nigeria were extracted from peer-reviewed journals and reports, geo-referenced and collated in a nationwide geographical information system database for the generation of point prevalence maps. This exercise revealed that the disease is endemic in 35 of the country's 36 states, including the federal capital territory of Abuja, and found in 462 unique locations out of 833 different survey locations. Schistosoma haematobium, the predominant species in Nigeria, was found in 368 locations (79.8%) covering 31 states, S. mansoni in 78 (16.7%) locations in 22 states and S. intercalatum in 17 (3.7%) locations in two states. S. haematobium and S. mansoni were found to be co-endemic in 22 states, while co-occurrence of all three species was only seen in one state (Rivers). The average prevalence for each species at each survey location varied between 0.5% and 100% for S. haematobium, 0.2% to 87% for S. mansoni and 1% to 10% for S. intercalatum. The estimated prevalence of S. haematobium, based on Bayesian geospatial predictive modelling with a set of bioclimatic variables, ranged from 0.2% to 75% with a mean prevalence of 23% for the country as a whole (95% confidence interval (CI): 22.8-23.1%). The model suggests that the mean temperature, annual precipitation and soil acidity significantly influence the spatial distribution. Prevalence estimates, adjusted for school-aged children in 2010, showed that the prevalence is <10% in most states with a few reaching as high as 50%. It was estimated that 11.3 million children require praziquantel annually (95% CI: 10.3-12.2 million).

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“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Habib

Guo

et al. 2016

Geospat Health

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Schistosomiasis is one of the most prevalent parasitic diseases impacting human health in the tropics and sub-tropics. The geographic distribution of Schistosoma mansoni, the most widespread species, includes areas in Africa, the Middle East, South America and the Caribbean. Snails of the genus Biomphalaria act as intermediate host for S. mansoni. Biomphalaria straminea is not indigenous in China but was accidentally introduced to Hong Kong from South America and has spread to other habitats in the southern parts of the country. This species is known for its great dispersal capacity that highlights the importance of the snail as a potential host for S. mansoni in China. In this connection, although no such infection has been recorded in the field so far, the continuous expansion of China's projects in endemic areas of Africa and import of the infection via returning workers or visitors deserve attention. The purpose of this study was to map and predict the spatial distribution of B. straminea in China. Snail occurrence data were assembled and investigated using MaxEnt software, along with climatic and environmental variables to produce a predictive risk map. Of the environmental variables tested, the precipitation of warmest quarter was the most contribution factor for snail's spatial distribution. Risk areas were found in southeastern China and it is expected that they will guide policies and control programmes to potential areas area of snail abundance and used for spatial targeting of control interventions for this invasive species.

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Large-scale determinants of intestinal schistosomiasis and intermediate host snail distribution across Africa: does climate matter?

Cited by 143 publications

References 58 publications

Global biogeographic regions in a human‐dominated world: the case of human diseases

Global biogeographic regions in a human‐dominated world: the case of human diseases

Mapping and prediction of schistosomiasis in Nigeria using compiled survey data and Bayesian geospatial modelling

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

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