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”

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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

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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

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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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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

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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.

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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.

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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.

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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