EPIDEMIOLOGY FOR URBAN MALARIA MAPPING
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1 TELE-EPIDEMIOLOGY EPIDEMIOLOGY FOR URBAN MALARIA Dukhan Vanessa Machault Observatoire Midi-Pyrénées, Laboratoire d Aérologie Pleiades days 17/01/2012
2 The concept of Tele-epidemiology The study of human or animal diseases transmitted by water, air or vectors - related to climatic, meteorological and environmental factors - using spatial information Ground data -> > main mechanisms Entomology Spatial distribution and dynamic of larval habitats Mosquito densities Mosquito flight range Parasitology, immunology, socio-economics Suitable satellite products Earth observation images Vegetation, soil, humidity indicators Land use and land cover Meteorological data (rainfall, temperature) Elevation Research of relationships - Modeling Spatial-temporal variability of data Hazard map Zone potentially occuped by mosquitoes (ZPOM) Vulnerability map Human or animal population Location, density Risk map 2 / 24
3 Objective To develop a robust operational methodology to draw dynamic high resolution malaria entomological risk maps in urban settings at two levels: - risk maps of the Anopheles breeding sites with larval productivity - risk maps of the Anopheles adult densities Based on the principles of tele-epidemiology: Ground: large data collection Remote sensing: appropriate data and images Modeling: spatial-temporal variability 3 / 24
4 Malaria transmission cycle Vector female mosquito Anopheles sp Parasite Plasmodium Stephen Luk - Spatial and temporal distribution - Speed of larval development - Adult survival Climate Environment Human Human Fusay - Speed of development 4 / 24 - Distribution and Vulnerability
5 Urban malaria Dakar, Senegal 60% of the world population will live in cities (2030) Epidemics of malaria (low parasite transmission, delayed acquired immunity) -> emerging disease of major importance 2.5 millions inhabitants in 2007 Sahelian climate Rainfall: mm (July - October) 5 / 24
6 Ground entomological data collection > km 45 areas (200 m x 200 m) 17 km Adult mosquitoes collection / 15 days ~ Anopheles gambiae s.l. Water bodies + larval habitats / 10 days ~300 larval habitats 6 / 24
7 Spatial and temporal heterogeneity in Dakar 40% 35% 30% 25% 20% 15% 10% Fraction of annual density Fraction of adult de la densité Anopheles annuelle d'anopheles adultes Fraction of annual Fraction de la densité density of Anopheles annuelle de larves larvaed'anopheles 5% 0% From 0 to 250 Anopheles bites/person/night Seasonal peak of Anopheles (rainy season) Need for spatial-temporal risk mapping 7 / 24
8 Dynamic operational mapping methodology Preliminary step Water bodies detection Water is mandatory for larvae Will the water bodies contain larvae? Where? When? Assistance for larval control Step 1 Anopheles larval densities mapping Assistance for mosquito control + information to the population Step 2 Adult Anopheles densities mapping At which scales the emerging mosquitoes will potentially bite humans? Hazard for vulnerable human population Mapping has been done at high spatial resolution (2.5m and 10m) and models were further tested for improvement using Pleiade-like data. 8 / 24
9 Earth Observation + meteorological data SPOT-5 images (2,5 m + 10 m 4 images) 26 september september 2008 * Vegetation, soil and humidity 28 september 2009 indicators 11 may 2009 * Land Use and Land Cover Digital Elevation Model - SRTM (90m 1 image) * Elevation MODIS images (1km weekly) * Land Surface Temperature (LST) Ground measurements (airport - daily) * Rainfall 9 / 24
10 Pleaide-like images (ORFEO-santé) Quickbird Rainy season PAN 2.4m MS World View Dry season m PAN 1.8m MS 10 / 24
11 Preliminary step: water detection Predict the water observed on the ground Logistic regression SPOT-5 200m Re scaled at 10m DEM NDWI rainy season (SPOT-5) NDVI dry season (SPOT-5) Built-up area (SPOT-5 classification) Elevation (DEM) Geographic Information System Detection of humidity Vegetation persisting during the dry season indicates the presence of water Absence of buildings favors larval habitats At low altitude, water table comes up to ground level in Dakar Biological meaning 11 / 24
12 Preliminary step: water detection Predict the water observed on the ground Logistic regression Pleiade-like image 200m Geographic Information System NDWI rainy season (SPOT-5) NDVI dry season (SPOT-5) Built-up area (SPOT-5 classification) Elevation (DEM) Direct detection of water Type of vegetation, trees, grass Other indices (other bands - blue) Detection of humidity Vegetation persisting during the dry season indicates the presence of water Absence of buildings favors larval habitats Improve detection of small water bodies Related or not with presence of water bodies Highlight other land covers 12 / 24
13 Preliminary step: water detection Detection of small water collections Non cemented wells in the market gardens 13 / 24
14 Preliminary step: water detection Land Cover: - High/low vegetation - High/middle/low urban density Land Use: - Residential / industry 14 / 24
15 Preliminary step: map of water bodies Inversion and extrapolation of the model to Dakar Yearly map Spatial resolution 10m 15 / 24
16 Step 1: prediction of the presence of larvae Predict the presence of larvae observed on the ground Logistic Stephen Stephen Luk SPOT-5 MODIS 200m Geographic Information System Mean in and around water bodies Land Surface Temperature (MODIS) Rainfalls (ground) NDWI + Soil BI (SPOT-5) Higher temp. speeds larval development Increases persistence of larval habitats Characterizes temporary water bodies and muddy bottom (favorable for larvae) Biological meaning 16 / 24
17 Step 1: prediction of the presence of larvae Predict the presence of larvae observed on the ground Logistic Stephen Stephen Luk Pleiade-like image MODIS 200m Variables associated with the presence of water in 10m grid Land Surface Temperature (MODIS) Rainfalls (ground) Geographic Information System Higher temp. speed larval development Increases persistence of larval habitats NDWI + Soil BI (SPOT-5) Characterizes temporary water bodies and muddy bottom (favorable for larvae) Type of vegetation, trees, grass Other indices (other bands - blue) Shade or surface vegetation Highlight other land covers 17 / 24
18 Step 1: map of presence of Anopheles larvae Inversion and extrapolation of the model to Dakar using the preliminary step Daily map Spatial resolution 10m 18 / 24
19 Step 2: prediction of Anopheles adult densities Predict the number of adults recorded on the ground Negative binomial Dukhan Preliminary step Step 1 Predicted larvae (Step 1) Rainfalls (ground) Mosquitoes survival rate 82% (from literature in Dakar) Built-up areas (SPOT-5 classification) Presence of larvae is related to adults Weights the predicted water surfaces Models the temporal decrease of the mosquito populations Dilution of bites in highly populated areas Biological meaning 19 / 24
20 Step 2: prediction of Anopheles adult densities Predict the number of adults recorded on the ground Negative binomial Dukhan Preliminary step Step 1 Predicted larvae (Step 1) Rainfalls (ground) Mosquitoes survival rate 82% (from literature near Dakar) Built-up Type of areas vegetation, (SPOT-5 trees, classification) grass Build-up areas Higher temp. speed larval development Weights the water surfaces from Step 1 Models the temporal decrease of the mosquito populations Dilution Survival of bites of in adult highly mosquitoes populated areas Refine population densities to model dilution of bites 20 / 24
21 Step 2: map of Anopheles adult densities Inversion and extrapolation of the model to Dakar 20 September 2009 Daily map Spatial resolution 10m 21 / 24
22 Conclusion Remotely sensed environmental and meteorological data can assist temporalspatial fine scale prediction and mapping of Anopheles densities in urban settings at 2 levels: - maps of Anopheles larvae - maps of Anopheles adult mosquitoes The methodology allows predicting future risk (Early Warning System) Guiding, planning and focusing malaria control (national hygiene services) 22 / 24
23 Conclusion Pleiades VHR images can improve mapping at each step of the methodology. Many possibilities for improvement of models will be tested soon, in the scope of EEOS-Malaria (Epidemiology Earth Observation Services Malaria) project, leaded by SIRS and SERTIT. Industrialization of the mapping process -> operational tools 23 / 24
24 Information / 24
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