Development of an Information System for the study, planning and control of schistosomiasis in the State of Minas Gerais, Brazil

Transcrição

1 Development of an Information System for the study, planning and control of schistosomiasis in the State of Minas Gerais, Brazil Fapemig EDP 1775/03, CRA 00070/04; CNPq grant # /03-8; /92-4; /2004-9);

2 Objectives - To establish a spatial relationship (Spatial Model) between the prevalence of schistosomiasis and/or the distribution of the intermediate hosts as a function of environmental variables. - To produce risk charts for prevalence or snails distribution for the entire Minas Gerais State.

3 Outline Main input data and definition of secondary explicative variables; Models types For prevalence risk estimation; For snail presence estimation; Strategies for variables geolocation and computation; Some Results Prevalence risk as a function of Social and meteorological variables; Remote sensing variables + snails presence; Indicative kriging for snails presence.

4 Main input data acquisition Prevalence data for 189 municipalities; Snails presence distribution for 286 municipalities; Moderated Resolution Imaging Spectro Radiometer (MODIS) imagery for Minas Gerais State (summer and winter); Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) acquired in 2000; Meteorological Data (precipitation, minimum and maximum temperature (summer and winter); Sociological Data (Human developing index, per capita income, education, longevity); Main Ecological Systems Chart.

5 Prevalence Source: MS - SESMG

6 Snails Incidence Source: Literature

7 Remote Sensing: MODIS H13V10, H13V11, H14V10, H14V11 Imagery acquisition for large areas with high temporal resolution and moderate (250 m) spatial resolution. Optical channels: Blue, Red, NIR, MIR, and Vegetation Indeces. Dates: Summer: 17/01 to 01/02/2002; Winter: 28/07 to 12/08/2002 NDVI = (NIR - V) / (NIT + V) EVI = 2,5*(NIR V) / (L + NIR + C1*V C2*B)

8 Remote Sensing: MODIS BLUE RED NIR MIR NDVI EVI

9 Remote Sensing: SRTM Shuttle Radar Topography Mission - 11 days mission in Feb 2000; - Interferometric technique; - Digital Topographic Model with 90 m resolution.

10 Meteorological Data: Precipitation Source: CPTEC/INPE Summer Precipitation: 17jan2002 until 01feb2002. Winter Precipitation: 28jul2002 until 12aug2002.

11 Meteorological Data: Minimum Temperature Source: CPTEC/INPE Summer Average: 17jan2002 until 01feb2002. Winter Average: 28jul2002 until 12aug2002.

12 Meteorological Data: Maximum Temperature Source: CPTEC/INPE Summer Average: 17jan2002 until 01feb2002. Winter Average: 28jul2002 until 12aug2002.

13 Sociological Data Source: Atlas do Desenvolvimento Humano no Brasil. Years: 1991 e 2000 Human Development Index (HDI); Per Capita Income; Education; Longevity. Total Number of Variables: 8

14 Main Ecological Systems Source: IBGE

15 Secondary explicative variables Linear Mixture Model Features from MODIS; Vegetation; Soil; Shadow. Topographic Features: from IBGE drainage charts: Mosaic from SRTM: Slope (degrees and percentage); Accumulation grid; Drainage Net; Hydrological Features (developed for this project) Water concentration index; Wetness index (potential, winter and summer); Puddle index (potential, winter and summer).

16 Linear mixture model results from MODIS Winter Summer

17 Hydrological Features Drainage Map IBGE mosaic: problems SRTM derived IBGE chart Drainage from SRTM

18 Water Concentration Index (WCI) WCI = (number of river cells inside a district) / (area of district) GEOMINAS SRTM

19 Wetness index (WI) log(accumulation grid +1)*(1-tan(slope))*(season precipitation) Dry Season (Winter) where 0 < slope 45 Rainy Season (Summer) Alternatives: 1. a WI based on WCI 2. Puddle Index (PI)

20 Outline Main input data and definition of secondary explicative variables; Models types For prevalence risk estimation; For snail presence estimation; Strategies for variables geolocation and computation; Results Prevalence risk as a function of Social and meteorological variables Remote sensing variables + snails presence Indicative kriging for snails presence

21 Model Types A. Prevalence Risk (PR) A1) PR = f(spatial variables) A2) PR = f(spatial variables, snails incidence) Obs: 1) Spatial Variables are any subset of primary or derived explicative variables mentioned before, except snails incidence; 2) Model A2 is expected to have higher explicative power but has the disadvantage of being dependent of the snails incidence knowledge. B. Snails Distribution (SD) B1) SD by indicator kriging. B2) SD = f(spatial variables) by logistic regression. Examples of models A1, A2 and B1 will be presented later.

22 Strategies for variables geolocation and computation Based on the facts that: Schistosomiasis has the characteristic of being local (dependent of snails incidence which depends themselves on hydrological collections). Prevalence data are given by municipalities, which does not properly reflect this local characateristic. Necessity of strategies for data geolocation and association with explanatory variables. Three strategies for prevalence association with explanatory variables: A) Association with average over municipality area. B) Several associations with average over selected sites inside the municipality area. C) Unique association with averages computed over all selected sites inside the municipality area.

23 A) Association with average over municipality area. Januaria Y 1 =a+b1*x1+b2*x2+...+bp*xp

24 B) Several associations with average over selected sites inside the municipality area RED POINTS ARE VILAGES AND TOWNS Sites Y 3 =a+b1*x1+b2*x2+...+bp*xp

25 C) Unique association with averages computed over all selected sites inside the municipality area. RED POINTS ARE VILAGES AND TOWNS / n Y 3 =a+b1*x1+b2*x2+...+bp*xp

26 Outline Main input data and definition of secondary explicative variables; Models types For prevalence risk estimation; For snail presence estimation; Strategies for variables geolocation and computation; Preliminary Results. (for single association with average over municipality area) Prevalence risk as a function of Social and meteorological variables Remote sensing variables + snails presence Indicative kriging for snails presence

27 Prevalence Estimation MODELS: 1- Social and Meteorological Variables 2- Remote Sensing Variables 3- Remote Sensing and Snails Incidence

28 Model 1 : Prevalence as a function of social and meteorological variables 15 Variables: Meteorological: (17/01_01/02/02 28/07_12/08/02) -> CPTEC PC = precipitation TN = minimal temperature TX = maximum temperature Sociological: ( ) -> PNUD IDH = global human development index IDH-R = human development index - renda IDH-L = human development index - longevidade IDH-E = human development index - education Qualitative: Vegetation type (VEG01-VEG02) -> Geominas - Caatinga (0-0) - Cerrado e Campo cerrado (1-0) - Floresta Atlântica (0-1)

29 Selected Variables: IDH-91, Summer Min Temp, Vegetation type E suas interações.

30 he Model 1 R²=0,3631 Estimated prevalence = e ( 0,20 0,21* TNv -4,40* IDH 91 1,15* VEG02 1,17*IDH91* VEG01) 1 Vegetation Type: -Forest Estimated prevalence = e (0,95 0,21* TNv -4,40* IDH 91) 1 -Cerrado Estimated prevalence = -Caatinga Estimated prevalence = e ( 0,20 0,21* TNv - 3,23* IDH91) e ( 0,20 0,21* TNv -4,40* IDH 91) 1 1

31 Comparison

32 Models 2 and 3 : Prevalence as a function of remote sensing variables with/without snails incidence knowledge. 20 Variables: MODIS: 17/01 to 01/02/02 (Summer) and 28/07 to 12/08/02 (Winter) EVI = enhanced vegetation index NDVI = normalized difference vegetation index Blue = Blue Channel Red = Red Channel NIR = Near Infrared Channel MIR= Medium Infrared Channel Veg = % of vegetation from Mixture Model Soil = % of soil from Mixture Model Shad = % of shadow from Mixture Model SRTM: SRTM = Altitude Slo = Slope (in degrees)

33 Model 2: Prevalence Estimation RS variables No Snail Knowledge y 1 = -2, ,00309*SRTM+10,41283*MIR-V+11,28594*NDVI-I R 2 =0,64

34 Model 3: Prevalence Estimation RS variables + snail incidence Any host species: MOL=0= no 1= yes ; other places: no knowledge PREV = exp [MOL*(-1, ,0018*SRTM+13,46913*MIR_V+7,49929*NDVI_I )] -1 R 2 = 0,84 Glabrata presence: MOL_G=0= no 1= yes ; other places: no knowledge. R^2 ~ 0.7 PREV = exp [0,5085+MOL_G*(-2, ,00162*SRTM+13,24955*MIR_V +7,54356 *NDVI_I)] -1 R 2 = 0,78

35 Estimated Prevalence Type 3 Glabrata Type 3 Any Species It is possible to combine with/without snails presence knowledge to change punctual estimations.

36 Snails distribution using indicative kriging for Sao Francisco Basin

37 Final Comments A very large data base was obtained for the purpose of this project. This data base can be further used for many other modeling studies for schistomiasis and even for other diseases. Some preliminary results are presented for prevalence risk estimation when environmental variables are average over entire municipalities areas. Higher R2 is obtained when snails incidence knowledge is combined with remote sensing variables. This high value is perhaps over-estimated because of the assumption of zero prevalence when no snails presence is affirmed. The usefulness of Hydrological Indices, developed for this project, will be still assessed for this and other possible diseases modeling, which are dependent on water collections. During the project development, due to the lack of uniformity on drainage charts patterns, a entire new drainage network for Minas Gerais had to be generated. FIOCRUZ

38 Some Project Publications Carvalho, O. S.; Dutra, L. V.; Moura, A. C. M.; Freitas, C. C.; Amaral, R. S.; Drummond, S. C.; Freitas, C. R.; Scholte, R. G. C.; Guimarães, R. J. P. S.; Melo, G. R.; Ragoni, V.; Guerra, M. Desenvolvimento de um sistema de informações para o estudo, planejamento e controle da esquistossomose no Estado de Minas Gerais. In: Simpósio Brasileiro de Sensoriamento Remoto, 12. (SBSR), abr. 2005, Goiânia. Anais... São José dos Campos: INPE, Comunicações, p CD-ROM, On-line. ISBN Dutra, L. V.; Freitas, C. R.; Moura, A. C. M.; Freitas, C. C.; Guimarães, R. J. P. S.; Amaral, R. S.; Drummond, S. C.; Scholte, R. G. C.; Melo, G. R.; Guerra, M.; Carvalho, O. S. Desenvolvimento de Índices Hidrológicos para aplicação em Sistema de Modelagem Ambiental de Risco de Esquistossomose em Minas Gerais. In: Simpósio Internacional sobre esquistossomose, set. 2005, Belo Horizonte. Anais Dutra, L. V.; Moura, A. C. M.; Freitas, C. R.; Freitas, C. C.; Amaral, R. S.; Drummond, S. C.; Guimarães, R. J. P. S.; Scholte, R. G. C.; Carvalho, O. S. Uso das imagens SRTM no mapeamento da rede de drenagem e no estudo da rugosidade do terreno em Minas Gerais e suas relações com a ocorrência da esquistossomose. In: Simpósio Nacional de Geografia da Saúde, nov. 2005, Rio de Janeiro. Anais (aceito). Freitas, C. C.; Guimarães, R. J. P. S.; Dutra, L. V.; Moura, A. C. M.; Amaral, R. S.; Drummond, S. C.; Scholte, R. G. C.; Guerra, M.; Freitas, C. R.; Carvalho, O. S. Análise e estimativa da prevalência da esquistossomose para o estado de Minas Gerais usando regressão múltipla com dados sócio-ambientais. In: Simpósio Internacional sobre esquistossomose, set. 2005, Belo Horizonte. Anais.

39 Some Project Publications Guimarães, R. J. P. S.; Freitas, C. C.; Dutra, L. V.; Shimabukuro, Y. E.; Carvalho, O. S.; Moura, A. C. M.; Amaral, R. S.; Drummond, S. C.; Scholte, R. G. C.; Freitas, C. R.; Melo, G. R. Comparação do modelo de mistura com os índices de vegetação NDVI e EVI oriundos das imagens MODIS para o Estado de Minas Gerais. In: Simpósio Brasileiro de Sensoriamento Remoto, 12. (SBSR), abr. 2005, Goiânia. Anais... São José dos Campos: INPE, Artigos, p CD-ROM, On-line. ISBN Guimarães, R. J. P. S.; Freitas, C. C.; Dutra, L. V.; Felgueiras, C. A.; Moura, A. C. M.; Amaral, R. S.; Drummond, S. C.; Scholte, R. G. C.; Freitas, C. R.; Carvalho, O. S. Distribuição espacial dos moluscos do gênero Biomphalaria na bacia do Rio São Francisco - MG usando krigeagem por indicação. In: Simpósio Internacional sobre esquistossomose, set. 2005, Belo Horizonte. Anais. Moura, A. C. M.; Freitas, C. R.; Dutra, L. V.; Melo, G. R.; Carvalho, O. S.; Freitas, C. C.; Amaral, R. S.; Scholte, R. G. C.; Drummond, S. C.; Guimarães, R. J. P. S. Atualização de mapa de drenagem como subsídio para montagem de SIG para a análise da distribuição da esquistossomose em Minas Gerais. In: Simpósio Brasileiro de Sensoriamento Remoto, 12. (SBSR), abr. 2005, Goiânia. Anais... São José dos Campos: INPE, Artigos, p CD-ROM, On-line. ISBN

40 Acknowledgments - Fapemig (grant # EDP 1775/03; CRA 0070/04); -CNPq (grant # /03-8; /92-4; /2004-9);