Use of Indicator Kriging to Investigate Schistosomiasis in Minas Gerais State, Brazil

Abstract

Geographic Information Systems (GISs) are composed of useful tools to map and to model the spatial distribution of events that have geographic importance as schistosomiasis. This paper is a review of the use the indicator kriging, implemented on the Georeferenced Information Processing System (SPRING) to make inferences about the prevalence of schistosomiasis and the presence of the species of Biomphalaria, intermediate hosts of Schistosoma mansoni, in areas without this information, in the Minas Gerais State, Brazil. The results were two maps. The first one was a map of Biomphalaria species, and the second was a new map of estimated prevalence of schistosomiasis. The obtained results showed that the indicator kriging can be used to better allocate resources for study and control of schistosomiasis in areas with transmission or the possibility of disease transmission.

Modelling of the distribution of Biomphalaria glabrata and Biomphalaria straminea in the metropolitan region of Recife, Pernambuco, Brazil

“…The replacement process of B. glabrata by B. straminea in the MRR was reported by Barbosa et al in 2014. The uncertainty found in this study was very similar to that seen by Guimarães et al in 2012. These authors reported a mean of 0.23 in estimates with greater certainty (100% in the São Francisco River Basin) and 0.33 in estimates with lower certainty (67% in the Paraíba do Sul River Basin), which supports the view that transitions and uncertainties will be greater in areas in which several species occur simultaneously.…”

“…In particular, these techniques have been used to model diseases such as schistosomiasis in studies developed by Guimarães et al (2009Guimarães et al ( , 2010Guimarães et al ( , 2012 to estimate the spatial distribution of Biomphalaria snails in the state of Minas Gerais. Furthermore, Scholte et al (2012) used modelling based on environmental characteristics with this aim in the whole country.…”

Spatial variations of pulmonary tuberculosis prevalence co-impacted by socio-economic and geographic factors in People’s Republic of China, 2010

“…Obtaining an accurate prediction is the ultimate aim of most studies that use kriging or cokriging. To improve the accuracy, many studies always selected a kriging or cokriging method they thought fit, or compared two or more kriging or cokriging methods to find the fittest one [ 5 - 8 , 25 ]. However, it is difficult to find the best fitness method that can provide the most accurate prediction because four cross-validation prediction error parameters can hardly meet requires at the same time in a method when many methods are compared.…”

“…If there are spatial dependencies the variance between the observations on two points normally increases with increasing distance until at a specific range a maximum value is reached. Considered to be the most sophisticated geostatistical method, kriging can potentially provide the most accurate results of continuous surface estimates, and has been more and more often used for epidemiological mapping of infectious disease, such as TB [ 4 ], schistosomiasis [ 5 ], malaria [ 6 ], cholera [ 7 ], dysentery [ 7 ] and influenza-like illness [ 8 ]. However, kriging is applied narrowly in discipline of TB control and prevention in P. R. China.…”

Estimating the spatial risk of tuberculosis distribution in Gurage zone, southern Ethiopia: a geostatistical kriging approach

“…It is known as the optimal interpolation method because it minimizes the mean square error of predictions and is statistically unbiased (i.e., estimated values and measured values agree on average) [ 12 ]. Recently, there have been several applications of kriging in the area of public health for estimating the predicted risk surface of infectious diseases, such as TB [ 3 ], malaria [ 11 ], cholera [ 13 ], helminths [ 14 ] and schistosomiasis [ 15 ].…”