Mapping Malaria Risk With Google Earth Engine For Effective Disease Control

[@YHWH979]

sir kindly share code

[@techhive.2023]

// Load the Tamil Nadu boundary var tamilNadu = ee.FeatureCollection('FAO/GAUL_SIMPLIFIED_500m/2015/level1') .filter(ee.Filter.eq('ADM1_NAME', 'Tamil Nadu')); // Center the map Map.centerObject(tamilNadu, 7); Map.addLayer(tamilNadu, {color: 'red'}, 'Tamil Nadu Boundary'); // Load environmental data // 1. Temperature (MODIS) var temperature = ee.ImageCollection('MODIS/061/MOD11A2') .filterDate('2023-01-01', '2023-12-31') .select('LST_Day_1km') .map(function(image) { return image.multiply(0.02).subtract(273.15) .copyProperties(image, ['system:time_start']); }); var meanTemperature = temperature.mean().clip(tamilNadu); // 2. Precipitation (CHIRPS) var precipitation = ee.ImageCollection('UCSB-CHG/CHIRPS/DAILY') .filterDate('2023-01-01', '2023-12-31'); var totalPrecipitation = precipitation.sum().clip(tamilNadu); // 3. Vegetation Index (NDVI from Sentinel-2) var sentinel2 = ee.ImageCollection('COPERNICUS/S2') .filterDate('2023-01-01', '2023-12-31') .filterBounds(tamilNadu) .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) .map(function(image) { var ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI'); return image.addBands(ndvi); }); var meanNDVI = sentinel2.select('NDVI').mean().clip(tamilNadu); // 4. Water Bodies (JRC Global Surface Water) var waterOccurrence = ee.ImageCollection('JRC/GSW1_4/MonthlyHistory') .select('water') // Corrected from 'occurrence' to 'water' .mean() .clip(tamilNadu); // Combine the variables into a single multiband image var predictors = meanTemperature.rename('Temperature') .addBands(totalPrecipitation.rename('Precipitation')) .addBands(meanNDVI.rename('NDVI')) .addBands(waterOccurrence.rename('WaterOccurrence')); // Visualize the environmental layers Map.addLayer(meanTemperature, {min: 20, max: 40, palette: ['blue', 'yellow', 'red']}, 'Mean Temperature'); Map.addLayer(totalPrecipitation, {min: 0, max: 1000, palette: ['white', 'blue']}, 'Total Precipitation'); Map.addLayer(meanNDVI, {min: 0, max: 1, palette: ['brown', 'green']}, 'Mean NDVI'); Map.addLayer(waterOccurrence, {min: 0, max: 100, palette: ['white', 'blue']}, 'Water Occurrence'); // Sample the predictors for training data // Add training points based on known malaria cases or hotspots in Tamil Nadu var malariaCases = ee.FeatureCollection([ ee.Feature(ee.Geometry.Point([78.1198, 11.6643]), {'Malaria': 1}), // Example: Salem ee.Feature(ee.Geometry.Point([78.7047, 10.7905]), {'Malaria': 1}), // Example: Trichy ee.Feature(ee.Geometry.Point([79.1325, 12.9716]), {'Malaria': 1}), // Example: Chennai ee.Feature(ee.Geometry.Point([77.1025, 11.2558]), {'Malaria': 0}), // Example: Coimbatore ee.Feature(ee.Geometry.Point([78.7047, 9.9252]), {'Malaria': 0}) // Example: Madurai ]); // Overlay predictors on malaria cases var trainingData = predictors.sampleRegions({ collection: malariaCases, properties: ['Malaria'], scale: 1000 }); // Train a Random Forest Classifier var classifier = ee.Classifier.smileRandomForest(50).train({ features: trainingData, classProperty: 'Malaria', inputProperties: ['Temperature', 'Precipitation', 'NDVI', 'WaterOccurrence'] }); // Classify the region var malariaRisk = predictors.classify(classifier).rename('MalariaRisk'); // Visualize predicted malaria risk Map.addLayer(malariaRisk, {min: 0, max: 1, palette: ['green', 'red']}, 'Malaria Risk'); // Export predicted malaria risk map Export.image.toDrive({ image: malariaRisk, description: 'TamilNadu_Malaria_Risk_Map', scale: 1000, region: tamilNadu, fileFormat: 'GeoTIFF' });