Spatial modeling of the schistosomiasis mansoni in Minas Gerais State, Brazil using spatial regression

Abstract

Schistosomiasis is a transmissible parasitic disease caused by the etiologic agent Schistosoma mansoni, whose intermediate hosts are snails of the genus Biomphalaria. The main goal of this paper is to estimate the prevalence of schistosomiasis in Minas Gerais State in Brazil using spatial disease information derived from the state transportation network of roads and rivers. The spatial information was incorporated in two ways: by introducing new variables that carry spatial neighborhood information and by using spatial regression models. Climate, socioeconomic and environmental variables were also used as co-variables to build models and use them to estimate a risk map for the whole state of Minas Gerais. The results show that the models constructed from the spatial regression produced a better fit, providing smaller root mean square error (RMSE) values. When no spatial information was used, the RMSE for the whole state of Minas Gerais reached 9.5%; with spatial regression, the RMSE reaches 8.8% (when the new variables are added to the model) and 8.5% (with the use of spatial regression). Variables representing vegetation, temperature, precipitation, topography, sanitation and human development indexes were important in explaining the spread of disease and identified certain conditions that are favorable for disease development. The use of spatial regression for the network of roads and rivers produced meaningful results for health management procedures and directing activities, enabling better detection of disease risk areas.

Understanding wetlands reclamation and soil-transmitted helminths and schistosomiasis incidence patterns in Rwanda (2001-2012)

“…These findings are consistent with existing knowledge on the environmental conditions required for the development of the intermediate host snails and hence, can influence transmission risk. The related physical and ecological parameters were similar to other studies (Alemayehu & Tomass, 2015;Fonseca, Freitas, Dutra, Guimarães, & Carvalho, 2014;Schur et al, 2013). Low elevation, valley wetlands, warm and humid conditions and less sandy soil create favorable conditions for S. mansoni, as host snails inhabit places with lower altitudes and wetlands with natural or cultivated vegetation (Alemayehu & Tomass, 2015;Bavia, Hale, Malone, Braud, & Shane, 1999).…”

Mapping Soil Transmitted Helminths and Schistosomiasis under Uncertainty: A Systematic Review and Critical Appraisal of Evidence

“…Data collection also influenced the results when there was a lack of spatial and laboratory sampled data in areas where the presence of infection was suspected to be high [66,72–74,80,100,103–105,111,125,130–132]. This could be due to inaccurate and missing reports [131], lack of people’s participation [132] and limited access to geographical areas [81].…”

A tale of two neglected tropical infections: using GIS to assess the spatial and temporal overlap of schistosomiasis and leprosy in a region of Minas Gerais, Brazil

“…Spatial analysis can detect clusters of disease that aggregate data can miss, thus making it very useful to study the transmission of infections (Clennon et al 2004, Fonseca et al 2014). Furthermore, it can take into account environmental factors that may influence disease incidence (Fonseca et al 2014).…”

“…Spatial analysis can detect clusters of disease that aggregate data can miss, thus making it very useful to study the transmission of infections (Clennon et al 2004, Fonseca et al 2014). Furthermore, it can take into account environmental factors that may influence disease incidence (Fonseca et al 2014). GIS has been useful to study both Hansen’s disease and schistosomiasis in Brazil and in other endemic areas (Sampaio et al 2013, Barreto et al 2014, Fonseca et al 2014, Lai et al 2015, Monteiro et al 2015).…”

“…Furthermore, it can take into account environmental factors that may influence disease incidence (Fonseca et al 2014). GIS has been useful to study both Hansen’s disease and schistosomiasis in Brazil and in other endemic areas (Sampaio et al 2013, Barreto et al 2014, Fonseca et al 2014, Lai et al 2015, Monteiro et al 2015). It has helped increase new case detection rates of leprosy in a hyperendemic area in Northern Brazil by allowing for targeted interventions in areas of clustering (Barreto et al 2015).…”