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MALARIA RISK ASSESSMENT USING GEOGRAPHIC INFORMATION SYSTEM (GIS):
A CASE OF ADAMA DISTRICT, Ethiopia
Kibrom Hailu Tafere (MSc.)
ESRI,EAST AFRICA Education GIS CON., SEP, 20169/30/2016 3:36:41 PM 1
A short Professional chronicle of “Kibrom Hailu”
9/30/2016 3:36:41 PM 2
Selected publication
Kibrom Hailu T., Tilahun E, and Daniel A,. GIS based Malaria Risk Assessment A case Study of
Adama District, Ethiopia, 2012. LAP LAMBERT Academic Publishing. ISBN: 978-3-659-21160-
7.Deutschland / Germany.
Kibrom Hailu T., Nesru M. Evaluation of Solid Waste Dumping Site using GIS & MCE A case
study of Addis Ababa, Ethiopia, 2016. LAP LAMBERT Academic Publishing. ISBN: 978-3-659-
91076-0. Deutschland / Germany.
Conference Experience South East Asia Survey Congress June 18 - 20, 2013, Manila, Phillipiness "Spatially-
Enabled Society I, Technical Session-I, participated as technical speaker.
Federation of International Geodesy (FIG) Surveyor, Training on Reference Frame in
Practice, Commission-5: Positioning and Measurement, June 21-22, 2013, Manila,
Phillipiness.
Reasarch Practice Solid Waste Dumping Site Selection using Multi Criteria Evaluation (MCE) and GIS techniques
for Adama Municipality, Ethiopia, 2010.
Safe removal of Solid Waste using Re-engineered design of inclination of chimney for Adama
Science and Technology University, Ethiopia, 2012.
Assessment of flood risk using Geographic Information System (GIS): a case study of kebele
09 in adama city
Assessment of sedimentation risk of lake koka using Geographic Information System (GIS)
Soil Erosion prediction, prioritization and managment employing SWAT and RUSLE model
using Geospatial techniques in Awash watershed
Modeling fish biomass for availability and optimal fishing rate in lake Awassa using Remote
Sensing and Geographic Information System (GIS)
Presentation out line
• Introduction
• Methods
• Results and Discussions
• Conclusions and Recommendations
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Risk - the Consequence of a specified hazardous event.
Hazard - a situation with a potential for harm.
Elements at Risk - Human population living in a geographical area
where locally acquired malaria cases occur.
Vulnerability - is the exposure of a given element.
Malarias – the area affected by malaria
9/30/2016 4
Definitions and Options
Risk assessment - Overall process of risk analysis
GIS- set of tools for collecting, storing, retrieving, transforming,
and displaying spatial data from the real world for a particular
set of purposes.
Rescaling Option:
1 to 5 by 1, 1 implies very low level and 5 vice versa.
5 4 3 2 1
Definitions Cont’d
9/30/2016 3:36:41 PM 6
Malaria is the fifth leading cause of death in the world.
40% of the world's population living at risk of malaria.
Malaria kills an African child every 30 seconds.
Also a primary cause of poverty.
More than USD12 billion loss of GDP every year.
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Introduction
Areas below 2000 m a.m.s.l are considered as vulnerable for
malaria.
In Ethiopia, 68% of the total population lives in areas at risk of
malaria.
In Adama, 93% of the district are hazardous for malaria.
The lack of geo-referenced spatial information to assess
malaria hazard and risk level for administration units in the
district.
* a.m.s.l = above mean sea level
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3:36:41 PM8
Introduction Cont’d
Accordingly, Malaria prevalence is studied by considering three
parameters.
Hazard, vulnerability and element at risk
as per Shook Risk Model (1997) the three parameters are
integrated in GIS environment.
*GIS – Geographical Information System.
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9
Introduction Cont’d
Research Questions:
1. What are the malaria hazard and risk level of Adama district in
general?
2. What are the malaria hazard and risk level each kebele and
land use?
Introduction Cont’d
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Objectives:
To assess malaria risk in Adama District using GIS.
To develop spatial model for designing the malaria hazard
and risk level assessment.
To prepare malaria hazard and risk map of Adama District.
To produce tabulated data areas of malaria risk and hazard
maps with land use and kebele layers respectively.
Introduction Cont’d
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General Description
• Study area: Adama District
• Location
(511737.875, 910052.511)min
(546432.813, 964326.375)max
• Altitude: 1500 to 2300m a.m.s.l
• Area is 77779ha.
• Mean annual rainfall of 740mm.
• Temperature ranges 10oc to 32oc.
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Study area
Factor development:
By a common rescaling option the causative factors were
developed for malaria prevalence.
Hazard (Agro-Ecology Zone, Soil, Slope and River distance),
Vulnerability (Wetness-index, Land Use and Lake distance), Element
at risk (Population) and Risk were developed and analyzed.
Over lay analysis:
Taking head of Shook Risk Model the 3 parameters were overlaid.
*MR = Malaria Risk *MH = Malaria Hazard , *V = Vulnerability , *Er = Element at risk
Methods
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MR = MH x V x Er
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IDW
Slope
Slope
Weig
hte
d O
verla
y
Malaria
Risk
Map
Pop.
Density
Element at
RiskPopulation
Reclassification
RiverMalaria
Hazard
Map
DEM
Reclassification Euclidean Distance
Soil
Weighted Overlay
AEZ
Rasterization
AEZ Soil River
Reclassification
River
Slope AEZ Soil
Recla
ssific
atio
nLake
LU
WI
DEM
LU
WI
Lake
Lake
Weig
hte
d O
verla
yMalaria
Vulnerability
Map
Euclid
ean
River
Schematic representation of malaria risk analysis
9/30/2016 3:36:41 PM 15
Risk
Vs
LU
Kebele
Hazard
Vs
Kebele
Risk
Vs
Kebele
Land
Use
Malaria
Hazard
Hazard
Vs
LU
Malaria
Risk
Tabulate
AreaTabulate
Area
Tabulate
Area
Tabulate
Area
Methods Tabulation area analysis:
The generated outputs of Adama District malaria risk assessment werecross tabulated with Kebele & LU for further investigations.
*LU = Land Use, *Kebele = small administration unit * Vs = inter comparison
There are spatial overlay analysis and tabulation area analysis results
generated in the research.
Spatial overlay analysis Result:
1. Malaria Hazard Assessment
0.1% of the area is rated as low hazardous due to coarse
texture soils (like pheozems) and steep slope factors.
84.87% of the area is rated as high to very high hazardous due
to soils with heavy or clay textured (Mollic & Vertic Andosols) and
AEZ (warm-semi arid & warm-sub moist lowlands) factors.
Results and Discussions
9/30/2016 3:36:41 PM 16*AEZ = Agro Ecology Zone
Malaria Hazard Assessment
Ranks of
Hazard Area (ha)
Hazard level
(%)
Low 78.910243 0.10
Moderate 11682.596 15.02
High 45084.179 57.96
Very High 20933.315 26.91
Total Area 77779 100
WAEZ=54%
WSOIL=12%
WSLOPE=28%
WRD=6%
Results Cont’d
The Consistency Ratio was 0.07,
Which is acceptable as the values <= 0.1
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2. Malaria Risk Assessment
Low malaria risk areas (68.97% ) are highly influenced by
elements at risk (due to less population density) factor level .
30.97% of the area is rated as moderate to very high risk areas.
due to the associated hazard & element at risk level.
Results Cont’d
Malaria Risk Assessment
All factors have equal
weight of importance
Ranks of Risk Area (ha) Risk (%)
Very Low 49.14 0.06
Low 53641.75 68.97
Moderate 22951.41 29.51
High 1136.51 1.46
Very High 0.19 0.00024
Total Area 77779 100
Results Cont’d
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Tabulation area analysis Result:
1. Hazard Vs Land Use
87% to 91% of the high & very high hazard level areas are
covered by cultivated land.
Grassland (42%) has the lowest value that reduce the hazard
level due to the limiting slope factor.
Water (0%) has got the lowest value under very high category
due to the nature of water bodies.
Results Cont’d
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Id Land Use
Low Moderate High Very High
Area
(ha) % Area (ha) % Area (ha) % Area (ha) %
1
Shrub
Land 0 0 0 0 3332.67 7.38 1462.71 6.99
2
Grass
Land 0 0 0 0 181.15 0.40 252.23 1.21
3
Cultivated
Land 75.85 100 10985.68 94.29 39334.95 87.14 19194.61 91.80
4 Water 0 0 665.73 5.71 2293.38 5.08 0 0
Results Cont’d
Hazard Vs Land Use
2. Hazard Vs Kebele
According to ADHO, only three kebeles Bubisa Kusaya, Laku
Balchi, and Mukiye Haro are found to be non-malarias.
All the rest 38 kebeles to be malarias ones.
*ADHO = Adama District Health Office9/30/2016 3:36:41 PM 22
Results Cont’d
due to the distance from river and soil factors influence on
malaria hazard
The study confirms that only Bubisa Kusaya and laku Balchi
are non-malarias all the rest 39 kebeles are malarias.
This shows that the study result is in strong agreement with
the official data.
9/30/2016 3:36:41 PM 23
Results Cont’d
3. Malaria Risk Vs Land Use
99% of Shrub land & grassland are categorized under low risk
level due to less population density in the area.
79% Cultivated land & 78% water bodies are categorized under
moderate risk level due to the higher population density.
9/30/2016 3:36:41 PM 24
Results Cont’d
Id Land Use
Very Low Low Moderate High Very High
Area
(ha) %
Area
(ha) %
Area
(ha) %
Area
(ha) %
Area
(ha) %
1 Shrub Land 0 0 4059.87 5.22 736.07 0.95 0 0 0 0
2 Grass Land 0 0 2930.26 3.77 29.15 0.04 0 0 0 0
3
Cultivated
Land 49.14 100 46554.29 59.89 21850.13 28.09 1136.51 1.46 0.19 0.00024
4 Water 0 0 97.33 0.13 336.07 0.43 0 0 0 0
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Results Cont’d
Malaria Risk Vs Land Use
4. Malaria Risk Vs Kebele
As to malaria risk levels one kebele (1%) found to be high,
11kebeles (27%) found to be moderate and 29 kebeles (71%)
found to be low risk.
In the study area Wonji Gefersa Town has got the high & very
high risk level of all kebeles.
due to high, very high hazard and very high element at risk
level.
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Results Cont’d
The positional accuracy assessment:
using ground truth data collected by GPS Receiver.
The method employed to examine the positional accuracy was
RMSE.
which tests the difference between observed and estimated,
coordinates of geographic realities.
the result depicts that the RMSE value is 0.013, which is
acceptable from 0.01 allowable threshold values.
9/30/2016 3:36:41 PM 27* RMSE =Residual Mean Square Error, * GPS = Global Positioning System
Result Validation of Malaria Risk Assessment
The attribute accuracy assessment:
carried out by using correlation analysis on the surveillance
epidemiological and the computed malaria hazard status data.
This analysis implies the nature and strength of relationship
between the two datasets.
the correlation coefficient (r) value must be between -1 and +1
inclusive.
the result depicts that is +0.805 which is positive and strong relation
between the two data.
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Result Validation of Malaria Risk Assessment
Conclusions:
The study reveals that GIS is can be used for modeling malaria
hazard and risk spatially for better assessment of the disease
prevalence in the area.
regarding malaria hazard levels, 2 kebeles are found to be non-
hazardous and the rest 39 kebeles are hazardous.
Concerning malaria risk levels 1% found to be as high risk, 27%
as moderate risk and the rest 71% as low risk areas from malaria
prevalence point of view.
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Conclusions and Recommendations
Recommendations:
Adama district health office should utilize GIS better than the
current situation in the future to find a remedy for malaria
combating activities.
for the successful application of GIS, there is a need to build
spatial database for all relevant features regarding malaria risk
assessment in the area.
9/30/2016 3:36:41 PM 30
Conclusions and Recommendations
Meteorological Data limitation is one of the reason for using AEZ
data for malaria hazard assessment.
AEZ data is not as precise as the meteorological data in locating
malarias areas in the district.
Validation of Malaria hazard assessment was carried out using
the secondary data collected from ADHO.
a field surveillance data is more precise in validating the computed
hazard status of the district.
However, it was not carried out due to financial limitations.
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Limitation of the study
*AEZ=Agro-Ecology Zone, * Adama District Health Office
FOREVER ETHIOPIA, AFRICA!!
Ethio-SAT, 2035 , Innovation make a difference! 9/30/2016 3:36:41 PM 32