Use of thermal and vegetation index data from earth observing satellites to evaluate the risk of schistosomiasis in Bahia, Brazil

Abstract

A geographic information system (GIS) was constructed using maps of regional agroclimatic features, vegetation indices and earth surface temperature data from environmental satellites, together with Schistosoma mansoni prevalence records from 270 municipalities including snail host distributions in Bahia, Brazil to study the spatial and temporal dynamics of infection and to identify environmental factors that influence the distribution of schistosomiasis. In an initial analysis, population density and duration (months) of the annual dry period were shown to be important determinants of disease. In cooperation with the National Institute of Spatial Research in Brazil (INPE), day and night imagery data covering the state of Bahia were selected at approximately bimonthly intervals in 1994 (six day-night pairs) from the data archives of the advanced very high resolution radiometer (AVHRR) sensor of the National Oceanic and Atmospheric Administration (NOAA)-11 satellite. A composite mosaic of these images was created to produce maps of: (1) average values between 0 and +1 of the normalized difference vegetation index (NDVI); and (2) average diurnal temperature differences (dT) on a scale of values between 0 and 15 degrees C. For each municipality, NDVI and dT were calculated for a 3x3 pixel (9 km(2) area) grid and analyzed for relationships to prevalence of schistosomiasis. Results showed a statistically significant relationship of prevalence to dT (rho=-0.218) and NDVI (rho=0.384) at the 95% level of confidence by the Spearman rank correlation coefficient. Results support use of NDVI, dT, dry period climatic stress factors and human population density for development of a GIS environmental risk assessment model for schistosomiasis in Brazil.

The Estrada Real project and endemic diseases: the case of schistosomiasis, geoprocessing and tourism

“…These results, compared to other work, suggest a successful role for Remote Sensing data applications in investigating the spatial epidemiology and control of major human helminths (Hassan et al 1998a, b, Brooker & Michael 2000, Bavia et al 2001, Beck et al 2002, Brooker 2002. Second and more importantly, our approach facilitated the geographical identification of priority areas for intervention, providing the possibility for more efficient control or surveillance efforts.…”

“…GIS was used in a larger scale schistosomiasis study in Asia (Cross et al 1996, Seto et al 2002, Yang et al 2005, Zhang et al 2008) and in several African countries, including Ethiopia , Egypt (Abdel-Rahman et al 2001, Malone et al 1994, Uganda (Kabatereine et al 2004), Tanzania (Brooker et al 2001) and Chad (Beasley et al 2002, Brooker 2002). In the state of Bahia, Brazil, Bavia et al (2001) conducted one of the first studies that attempted to correlate disease distribution with environmental variables. Other studies in Brazil using GIS were conducted in Pernambuco (Barbosa et al 2004) and MG , 2009).…”

“…(GIS) in the study of schistosomiasis -Because schistosomiasis is a disease determined in space and time by certain risk factors, the GIS is a very powerful tool for better understanding disease prevalence and the distri-bution of multiple risk factors. The use of both GIS and statistical techniques allows for the determination of risk factors and geolocalization of risk areas and may lead to the optimization of resources and choosing the best strategies for disease control (Beck et al 1997, Bavia et al 2001.…”

Evaluation of a linear spectral mixture model and vegetation indices (NDVI and EVI) in a study of schistosomiasis mansoni and Biomphalaria glabrata distribution in the state of Minas Gerais, Brazil

“…Here, environmental factors were characterised using satellite imagery and spatial analysis. Increasingly, research into the link between the disease, environmental conditions, geographical information systems and remote sensing data is being conducted and this paper may play an important role in schistosomiasis studies (Beck et al 1997, Bavia et al 2001.…”

“…In Brazil, the first studies were performed in the state of Bahia and attempted to correlate the disease distribution with Normalized Difference Vegetation Index (NDVI), diurnal temperature difference and the length of the annual dry period (Bavia et al 2001). Other studies in Brazil using GIS were conducted in Pernambuco (Barbosa et al 2004) and MG (Carvalho et al 2005, 2008, Martins 2008.…”