// Section 1: Data Import
var woredas=ee.FeatureCollection(
   'projects/gee-book/assets/A1-6/amhara_woreda_20170207');
// Create region outer boundary to filter products on.
var amhara=woredas.geometry().bounds();
var gpm=ee.ImageCollection('NASA/GPM_L3/IMERG_V06');
var LSTTerra8=ee.ImageCollection('MODIS/061/MOD11A2')
   // Due to MCST outage, only use dates after this for this script.
   .filterDate('2001-06-26', Date.now());
var brdfReflect=ee.ImageCollection('MODIS/006/MCD43A4');
var brdfQa=ee.ImageCollection('MODIS/006/MCD43A2');

// Visualize woredas with black borders and no fill.
// Create an empty image into which to paint the features, cast to byte.
var empty=ee.Image().byte();
// Paint all the polygon edges with the same number and width.
var outline=empty.paint({
   featureCollection: woredas,
   color: 1,
   width: 1
});
// Add woreda boundaries to the map.
Map.setCenter(38, 11.5, 7);
Map.addLayer(outline,{
   palette: '000000'
}, 'Woredas');

Fig. A1.6.1 Woreda (district) boundaries in the Amhara region of Ethiopia

// Section 2: Handling of dates

// 2.1 Requested start and end dates.
var reqStartDate=ee.Date('2021-10-01');
var reqEndDate=ee.Date('2021-11-30');

// 2.2 LST Dates
// LST MODIS is every 8 days, and a user-requested date will likely not match.
// We want to get the latest previous image date,
//  i.e. the date the closest, but prior to, the requested date.
// We will filter later.
// Get date of first image.
var LSTEarliestDate=LSTTerra8.first().date();
// Filter collection to dates from beginning to requested start date.
var priorLstImgCol=LSTTerra8.filterDate(LSTEarliestDate,
   reqStartDate);
// Get the latest (max) date of this collection of earlier images.
var LSTPrevMax=priorLstImgCol.reduceColumns({
   reducer: ee.Reducer.max(),
   selectors: ['system:time_start']
});
var LSTStartDate=ee.Date(LSTPrevMax.get('max'));
print('LSTStartDate', LSTStartDate);

// 2.3 Last available data dates
// Different variables have different data lags.
// Data may not be available in user range.
// To prevent errors from stopping script,
//  grab last available (if relevant) & filter at end.

// 2.3.1 Precipitation
// Calculate date of most recent measurement for gpm (of all time).
var gpmAllMax=gpm.reduceColumns(ee.Reducer.max(), [
   'system:time_start'
]);
var gpmAllEndDateTime=ee.Date(gpmAllMax.get('max'));
// GPM every 30 minutes, so get just date part.
var gpmAllEndDate=ee.Date.fromYMD({
   year: gpmAllEndDateTime.get('year'),
   month: gpmAllEndDateTime.get('month'),
   day: gpmAllEndDateTime.get('day')
});

// If data ends before requested start, take last data date,
// otherwise use requested date.
var precipStartDate=ee.Date(gpmAllEndDate.millis()
   .min(reqStartDate.millis()));
print('precipStartDate', precipStartDate);

// 2.3.2 BRDF
// Calculate date of most recent measurement for brdf (of all time).
var brdfAllMax=brdfReflect.reduceColumns({
   reducer: ee.Reducer.max(),
   selectors: ['system:time_start']
});
var brdfAllEndDate=ee.Date(brdfAllMax.get('max'));
// If data ends before requested start, take last data date,
// otherwise use the requested date.
var brdfStartDate=ee.Date(brdfAllEndDate.millis()
   .min(reqStartDate.millis()));
print('brdfStartDate', brdfStartDate);
print('brdfEndDate', brdfAllEndDate);

var naiveLstFilter=LSTTerra8.filterDate(reqStartDate, reqEndDate);
var naiveLstStart=naiveLstFilter.reduceColumns({
   reducer: ee.Reducer.min(),
   selectors: ['system:time_start']
});
var naiveLstStartDate=ee.Date(naiveLstStart.get('min'));
print('naiveLstStartDate', naiveLstStartDate);

// Section 3: Precipitation

// Section 3.1: Precipitation filtering and dates

// Filter gpm by date, using modified start if necessary.
var gpmFiltered=gpm
   .filterDate(precipStartDate, reqEndDate.advance(1, 'day'))
   .filterBounds(amhara)
   .select('precipitationCal');

// Calculate date of most recent measurement for gpm
// (in the modified requested window).
var gpmMax=gpmFiltered.reduceColumns({
   reducer: ee.Reducer.max(),
   selectors: ['system:time_start']
});
var gpmEndDate=ee.Date(gpmMax.get('max'));
var precipEndDate=gpmEndDate;
print('precipEndDate ', precipEndDate);

// Create a list of dates for the precipitation time series.
var precipDays=precipEndDate.difference(precipStartDate, 'day');
var precipDatesPrep=ee.List.sequence(0, precipDays, 1);

function makePrecipDates(n){
   return precipStartDate.advance(n, 'day');
}
var precipDates=precipDatesPrep.map(makePrecipDates);

// Section 3.2: Calculate daily precipitation

// Function to calculate daily precipitation:
function calcDailyPrecip(curdate){
   curdate=ee.Date(curdate);
   var curyear=curdate.get('year');
   var curdoy=curdate.getRelative('day', 'year').add(1);
   var totprec=gpmFiltered
       .filterDate(curdate, curdate.advance(1, 'day'))
       .select('precipitationCal')
       .sum()
       //every half-hour
       .multiply(0.5)
       .rename('totprec');

   return totprec
       .set('doy', curdoy)
       .set('year', curyear)
       .set('system:time_start', curdate);
}
// Map function over list of dates.
var dailyPrecipExtended=
   ee.ImageCollection.fromImages(precipDates.map(calcDailyPrecip));

// Filter back to the original user requested start date.
var dailyPrecip=dailyPrecipExtended
   .filterDate(reqStartDate, precipEndDate.advance(1, 'day'));

// Section 3.3: Summarize daily precipitation by woreda

// Filter precip data for zonal summaries.
var precipSummary=dailyPrecip
   .filterDate(reqStartDate, reqEndDate.advance(1, 'day'));

// Function to calculate zonal statistics for precipitation by woreda.
function sumZonalPrecip(image){
   // To get the doy and year,
   // convert the metadata to grids and then summarize.
   var image2=image.addBands([
       image.metadata('doy').int(),
       image.metadata('year').int()
   ]);
   // Reduce by regions to get zonal means for each county.
   var output=image2.select(['year', 'doy', 'totprec'])
       .reduceRegions({
           collection: woredas,
           reducer: ee.Reducer.mean(),
           scale: 1000
       });
   return output;
}
// Map the zonal statistics function over the filtered precip data.
var precipWoreda=precipSummary.map(sumZonalPrecip);
// Flatten the results for export.
var precipFlat=precipWoreda.flatten();

// Section 4: Land surface temperature

// Section 4.1: Calculate LST variables

// Filter Terra LST by altered LST start date.
// Rarely, but at the end of the year if the last image is late in the year
//  with only a few days in its period, it will sometimes not grab
//  the next image. Add extra padding to reqEndDate and
//  it will be trimmed at the end.
var LSTFiltered=LSTTerra8
   .filterDate(LSTStartDate, reqEndDate.advance(8, 'day'))
   .filterBounds(amhara)
   .select('LST_Day_1km', 'QC_Day', 'LST_Night_1km', 'QC_Night');

// Filter Terra LST by QA information.
function filterLstQa(image){
   var qaday=image.select(['QC_Day']);
   var qanight=image.select(['QC_Night']);
   var dayshift=qaday.rightShift(6);
   var nightshift=qanight.rightShift(6);
   var daymask=dayshift.lte(2);
   var nightmask=nightshift.lte(2);
   var outimage=ee.Image(image.select(['LST_Day_1km',
       'LST_Night_1km'
   ]));
   var outmask=ee.Image([daymask, nightmask]);
   return outimage.updateMask(outmask);
}
var LSTFilteredQa=LSTFiltered.map(filterLstQa);

// Rescale temperature data and convert to degrees Celsius (C).
function rescaleLst(image){
   var LST_day=image.select('LST_Day_1km')
       .multiply(0.02)
       .subtract(273.15)
       .rename('LST_day');
   var LST_night=image.select('LST_Night_1km')
       .multiply(0.02)
       .subtract(273.15)
       .rename('LST_night');
   var LST_mean=image.expression(
       '(day + night) / 2',{
           'day': LST_day.select('LST_day'),
           'night': LST_night.select('LST_night')
       }
   ).rename('LST_mean');
   return image.addBands(LST_day)
       .addBands(LST_night)
       .addBands(LST_mean);
}
var LSTVars=LSTFilteredQa.map(rescaleLst);

// Section 4.2: Calculate daily LST

// Create list of dates for time series.
var LSTRange=LSTVars.reduceColumns({
   reducer: ee.Reducer.max(),
   selectors: ['system:time_start']
});
var LSTEndDate=ee.Date(LSTRange.get('max')).advance(7, 'day');
var LSTDays=LSTEndDate.difference(LSTStartDate, 'day');
var LSTDatesPrep=ee.List.sequence(0, LSTDays, 1);

function makeLstDates(n){
   return LSTStartDate.advance(n, 'day');
}
var LSTDates=LSTDatesPrep.map(makeLstDates);

// Function to calculate daily LST by assigning the 8-day composite summary
// to each day in the composite period:
function calcDailyLst(curdate){
   var curyear=ee.Date(curdate).get('year');
   var curdoy=ee.Date(curdate).getRelative('day', 'year').add(1);
   var moddoy=curdoy.divide(8).ceil().subtract(1).multiply(8).add(
       1);
   var basedate=ee.Date.fromYMD(curyear, 1, 1);
   var moddate=basedate.advance(moddoy.subtract(1), 'day');
   var LST_day=LSTVars
       .select('LST_day')
       .filterDate(moddate, moddate.advance(1, 'day'))
       .first()
       .rename('LST_day');
   var LST_night=LSTVars
       .select('LST_night')
       .filterDate(moddate, moddate.advance(1, 'day'))
       .first()
       .rename('LST_night');
   var LST_mean=LSTVars
       .select('LST_mean')
       .filterDate(moddate, moddate.advance(1, 'day'))
       .first()
       .rename('LST_mean');
   return LST_day
       .addBands(LST_night)
       .addBands(LST_mean)
       .set('doy', curdoy)
       .set('year', curyear)
       .set('system:time_start', curdate);
}
// Map the function over the image collection
var dailyLstExtended=
   ee.ImageCollection.fromImages(LSTDates.map(calcDailyLst));

// Filter back to original user requested start date
var dailyLst=dailyLstExtended
   .filterDate(reqStartDate, LSTEndDate.advance(1, 'day'));

// Section 4.3: Summarize daily LST by woreda

// Filter LST data for zonal summaries.
var LSTSummary=dailyLst
   .filterDate(reqStartDate, reqEndDate.advance(1, 'day'));
// Function to calculate zonal statistics for LST by woreda:
function sumZonalLst(image) {
   // To get the doy and year, we convert the metadata to grids
   //  and then summarize.
   var image2=image.addBands([
       image.metadata('doy').int(),
       image.metadata('year').int()
   ]);
   // Reduce by regions to get zonal means for each county.
   var output=image2
       .select(['doy', 'year', 'LST_day', 'LST_night', 'LST_mean'])
       .reduceRegions({
           collection: woredas,
           reducer: ee.Reducer.mean(),
           scale: 1000
       });
   return output;
}
// Map the zonal statistics function over the filtered LST data.
var LSTWoreda=LSTSummary.map(sumZonalLst);
// Flatten the results for export.
var LSTFlat=LSTWoreda.flatten();

// Section 5: Spectral index NDWI

// Section 5.1: Calculate NDWI

// Filter BRDF-Adjusted Reflectance by date.
var brdfReflectVars=brdfReflect
   .filterDate(brdfStartDate, reqEndDate.advance(1, 'day'))
   .filterBounds(amhara)
   .select([
           'Nadir_Reflectance_Band1', 'Nadir_Reflectance_Band2',
           'Nadir_Reflectance_Band3', 'Nadir_Reflectance_Band4',
           'Nadir_Reflectance_Band5', 'Nadir_Reflectance_Band6',
           'Nadir_Reflectance_Band7'
       ],
       ['red', 'nir', 'blue', 'green', 'swir1', 'swir2', 'swir3']);

// Filter BRDF QA by date.
var brdfReflectQa=brdfQa
   .filterDate(brdfStartDate, reqEndDate.advance(1, 'day'))
   .filterBounds(amhara)
   .select([
           'BRDF_Albedo_Band_Quality_Band1',
           'BRDF_Albedo_Band_Quality_Band2',
           'BRDF_Albedo_Band_Quality_Band3',
           'BRDF_Albedo_Band_Quality_Band4',
           'BRDF_Albedo_Band_Quality_Band5',
           'BRDF_Albedo_Band_Quality_Band6',
           'BRDF_Albedo_Band_Quality_Band7',
           'BRDF_Albedo_LandWaterType'
       ],
       ['qa1', 'qa2', 'qa3', 'qa4', 'qa5', 'qa6', 'qa7', 'water']);

// Join the 2 collections.
var idJoin=ee.Filter.equals({
   leftField: 'system:time_end',
   rightField: 'system:time_end'
});
// Define the join.
var innerJoin=ee.Join.inner('NBAR', 'QA');
// Apply the join.
var brdfJoined=innerJoin.apply(brdfReflectVars, brdfReflectQa,
   idJoin);

// Add QA bands to the NBAR collection.
function addQaBands(image){
   var nbar=ee.Image(image.get('NBAR'));
   var qa=ee.Image(image.get('QA')).select(['qa2']);
   var water=ee.Image(image.get('QA')).select(['water']);
   return nbar.addBands([qa, water]);
}
var brdfMerged=ee.ImageCollection(brdfJoined.map(addQaBands));

// Function to mask out pixels based on QA and water/land flags.
function filterBrdf(image) {
   // Using QA info for the NIR band.
   var qaband=image.select(['qa2']);
   var wband=image.select(['water']);
   var qamask=qaband.lte(2).and(wband.eq(1));
   var nir_r=image.select('nir').multiply(0.0001).rename('nir_r');
   var swir2_r=image.select('swir2').multiply(0.0001).rename(
       'swir2_r');
   return image.addBands(nir_r)
       .addBands(swir2_r)
       .updateMask(qamask);
}
var brdfFilteredVars=brdfMerged.map(filterBrdf);

// Function to calculate spectral indices:
function calcBrdfIndices(image){
   var curyear=ee.Date(image.get('system:time_start')).get('year');
   var curdoy=ee.Date(image.get('system:time_start'))
       .getRelative('day', 'year').add(1);
   var ndwi6=image.normalizedDifference(['nir_r', 'swir2_r'])
       .rename('ndwi6');
   return image.addBands(ndwi6)
       .set('doy', curdoy)
       .set('year', curyear);
}
// Map function over image collection.
brdfFilteredVars=brdfFilteredVars.map(calcBrdfIndices);

// Section 5.2: Calculate daily NDWI

// Create list of dates for full time series.
var brdfRange=brdfFilteredVars.reduceColumns({
   reducer: ee.Reducer.max(),
   selectors: ['system:time_start']
});
var brdfEndDate=ee.Date(brdfRange.get('max'));
var brdfDays=brdfEndDate.difference(brdfStartDate, 'day');
var brdfDatesPrep=ee.List.sequence(0, brdfDays, 1);

function makeBrdfDates(n){
   return brdfStartDate.advance(n, 'day');
}
var brdfDates=brdfDatesPrep.map(makeBrdfDates);

// List of dates that exist in BRDF data.
var brdfDatesExist=brdfFilteredVars
   .aggregate_array('system:time_start');

// Get daily brdf values.
function calcDailyBrdfExists(curdate){
   curdate=ee.Date(curdate);
   var curyear=curdate.get('year');
   var curdoy=curdate.getRelative('day', 'year').add(1);
   var brdfTemp=brdfFilteredVars
       .filterDate(curdate, curdate.advance(1, 'day'));
   var outImg=brdfTemp.first();
   return outImg;
}
var dailyBrdfExtExists=
   ee.ImageCollection.fromImages(brdfDatesExist.map(
       calcDailyBrdfExists));

// Create empty results, to fill in dates when BRDF data does not exist.
function calcDailyBrdfFiller(curdate){
   curdate=ee.Date(curdate);
   var curyear=curdate.get('year');
   var curdoy=curdate.getRelative('day', 'year').add(1);
   var brdfTemp=brdfFilteredVars
       .filterDate(curdate, curdate.advance(1, 'day'));
   var brdfSize=brdfTemp.size();
   var outImg=ee.Image.constant(0).selfMask()
       .addBands(ee.Image.constant(0).selfMask())
       .addBands(ee.Image.constant(0).selfMask())
       .addBands(ee.Image.constant(0).selfMask())
       .addBands(ee.Image.constant(0).selfMask())
       .rename(['ndvi', 'evi', 'savi', 'ndwi5', 'ndwi6'])
       .set('doy', curdoy)
       .set('year', curyear)
       .set('system:time_start', curdate)
       .set('brdfSize', brdfSize);
   return outImg;
}
// Create filler for all dates.
var dailyBrdfExtendedFiller=
   ee.ImageCollection.fromImages(brdfDates.map(calcDailyBrdfFiller));
// But only used if and when size was 0.
var dailyBrdfExtFillFilt=dailyBrdfExtendedFiller
   .filter(ee.Filter.eq('brdfSize', 0));
// Merge the two collections.
var dailyBrdfExtended=dailyBrdfExtExists
   .merge(dailyBrdfExtFillFilt);

// Filter back to original user requested start date.
var dailyBrdf=dailyBrdfExtended
   .filterDate(reqStartDate, brdfEndDate.advance(1, 'day'));

// Section 5.3: Summarize daily spectral indices by woreda

// Filter spectral indices for zonal summaries.
var brdfSummary=dailyBrdf
   .filterDate(reqStartDate, reqEndDate.advance(1, 'day'));

// Function to calculate zonal statistics for spectral indices by woreda:
function sumZonalBrdf(image){
   // To get the doy and year, we convert the metadata to grids
   //  and then summarize.
   var image2=image.addBands([
       image.metadata('doy').int(),
       image.metadata('year').int()
   ]);
   // Reduce by regions to get zonal means for each woreda.
   var output=image2.select(['doy', 'year', 'ndwi6'])
       .reduceRegions({
           collection: woredas,
           reducer: ee.Reducer.mean(),
           scale: 1000
       });
   return output;
}

// Map the zonal statistics function over the filtered spectral index data.
var brdfWoreda=brdfSummary.map(sumZonalBrdf);
// Flatten the results for export.
var brdfFlat=brdfWoreda.flatten();

// Section 6: Map display of calculated environmental variables
var displayDate=ee.Date('2021-10-01');

var precipDisp=dailyPrecip
   .filterDate(displayDate, displayDate.advance(1, 'day'));
var brdfDisp=dailyBrdf
   .filterDate(displayDate, displayDate.advance(1, 'day'));
var LSTDisp=dailyLst
   .filterDate(displayDate, displayDate.advance(1, 'day'));

// Select the image (should be only one) from each collection.
var precipImage=precipDisp.first().select('totprec');
var LSTmImage=LSTDisp.first().select('LST_mean');
var ndwi6Image=brdfDisp.first().select('ndwi6');

// Palettes for environmental variable maps:
var palettePrecip=['f7fbff', '08306b'];
var paletteLst=['fff5f0', '67000d'];
var paletteSpectral=['ffffe5', '004529'];

// Add layers to the map.
// Show precipitation by default,
//  others hidden until users picks them from layers drop down.
Map.addLayer({
   eeObject: precipImage,
   visParams:{
       min: 0,
       max: 20,
       palette: palettePrecip
   },
   name: 'Precipitation',
   shown: true,
   opacity: 0.75
});
Map.addLayer({
   eeObject: LSTmImage,
   visParams:{
       min: 0,
       max: 40,
       palette: paletteLst
   },
   name: 'LST Mean',
   shown: false,
   opacity: 0.75
});
Map.addLayer({
   eeObject: ndwi6Image,
   visParams:{
       min: 0,
       max: 1,
       palette: paletteSpectral
   },
   name: 'NDWI6',
   shown: false,
   opacity: 0.75
});

Fig. A1.6.2 Calculated total daily precipitation overlaid on woreda boundaries in the Amhara region of Ethiopia

// Section 7: Exporting

// 7.1 Export naming
var reqStartDateText=reqStartDate.format('yyyy-MM-dd').getInfo();

// Precipitation
var precipPrefix='Export_Precip_Data';
var precipLastDate=ee.Date(reqEndDate.millis()
   .min(precipEndDate.millis()));
var precipSummaryEndDate=precipLastDate
   .format('yyyy-MM-dd').getInfo();
var precipFilename=precipPrefix
   .concat('_', reqStartDateText,
       '_', precipSummaryEndDate);
// LST
var LSTPrefix='Export_LST_Data';
var LSTLastDate=ee.Date(reqEndDate.millis()
   .min(LSTEndDate.millis()));
var LSTSummaryEndDate=LSTLastDate
   .format('yyyy-MM-dd').getInfo();
var LSTFilename=LSTPrefix
   .concat('_', reqStartDateText,
       '_', LSTSummaryEndDate);
// BRDF
var brdfPrefix='Export_Spectral_Data';
var brdfLastDate=ee.Date(reqEndDate.millis()
   .min(brdfEndDate.millis()));
var brdfSummaryEndDate=brdfLastDate
   .format('yyyy-MM-dd').getInfo();
var brdfFilename=brdfPrefix
   .concat('_', reqStartDateText,
       '_', brdfSummaryEndDate);

// 7.2 Export flattened tables to Google Drive
// Need to click 'RUN in the Tasks tab to configure and start each export.
Export.table.toDrive({
   collection: precipFlat,
   description: precipFilename,
   selectors: ['wid', 'woreda', 'doy', 'year', 'totprec']
});
Export.table.toDrive({
   collection: LSTFlat,
   description: LSTFilename,
   selectors: ['wid', 'woreda', 'doy', 'year',
       'LST_day', 'LST_night', 'LST_mean'
   ]
});
Export.table.toDrive({
   collection: brdfFlat,
   description: brdfFilename,
   selectors: ['wid', 'woreda', 'doy', 'year', 'ndwi6']
});

Fig. A1.6.3 Examples of the three CSV files returned from the script

// Section 8: Viewing external analyses results

// This is using *synthetic* malaria data.
// For demonstration only, not to be used for epidemiological purposes.
var epidemiaResults=ee.FeatureCollection(
   'projects/gee-book/assets/A1-6/amhara_pilot_synthetic_2018W32'
);
// Filter to only keep pilot woredas with forecasted values.
var pilot=epidemiaResults
   .filter(ee.Filter.neq('inc_n_fc', null));
var nonpilot=epidemiaResults
   .filter(ee.Filter.eq('inc_n_fc', null));

Map.setCenter(38, 11.5, 7);

// Paint the pilot woredas with different colors for forecasted* incidence
// fc_n_inc here is the forecasted incidence (cut into factors)
// made on (historical) 2018W24 (i.e. 8 weeks in advance).
// * based on synthetic data for demonstration only.
// Incidence per 1000
// 1 : [0 - 0.25)
// 2 : [0.25 - 0.5)
// 3 : [0.5 - 0.75)
// 4 : [0.75 - 1)
// 5 : > 1

var empty=ee.Image().byte();
var fill_fc=empty.paint({
   featureCollection: pilot,
   color: 'inc_n_fc',
});
var palette=['fee5d9', 'fcae91', 'fb6a4a', 'de2d26', 'a50f15'];
Map.addLayer(
   fill_fc,{
       palette: palette,
       min: 1,
       max: 5
   },
   'Forecasted Incidence'
);

// Paint the woredas with different colors for the observed* incidence.
// * based on synthetic data for demonstration only
var fill_obs=empty.paint({
   featureCollection: pilot,
   color: 'inc_n_obs',
});
var palette=['fee5d9', 'fcae91', 'fb6a4a', 'de2d26', 'a50f15'];
// Layer is off by default, users change between the two in the map viewer.
Map.addLayer(
   fill_obs,{
       palette: palette,
       min: 1,
       max: 5
   },
   'Observed Incidence',
   false
);

// Add gray fill for nonpilot woredas (not included in study).
var fill_na=empty.paint({
   featureCollection: nonpilot
});
Map.addLayer(
   fill_na, {
       palette: 'a1a9a8'
   },
   'Non-study woredas'
);

// Draw borders for ALL Amhara region woredas.
var outline=empty.paint({
   featureCollection: epidemiaResults,
   color: 1,
   width: 1
});
// Add woreda boundaries to map.
Map.addLayer(
   outline, {
       palette: '000000'
   },
   'Woredas'
);

Fig. A1.6.4 Visualization of forecasted malaria incidence for week 32 of 2018 made during week 24 (an eight-week lead time). Malaria data is synthetic, for demonstration purposes only. The incidence has been categorized into five categories (from lighter to dark red): 0–0.25, 0.25–0.5, 0.5–0.75, 0.75–1, and greater than 1. Only woredas in the pilot project have values; the rest of the Amhara region is marked in gray fill. Another layer available to view is the observed (synthetic) incidence rate for 2018W32.