MALARIA RISK ASSESSMENT USING GEOGRAPHIC INFORMATION SYSTEM (GIS): - - PowerPoint PPT Presentation

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, 2016

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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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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

Weighted Overlay

Malaria Risk Map Pop. Density Element at Risk Population

Reclassification

River Malaria Hazard Map DEM

Reclassification Euclidean Distance

Soil

Weighted Overlay

AEZ

Rasterization

AEZ Soil River

Reclassification

River Slope AEZ Soil

Reclassification

Lake LU WI DEM LU WI Lake Lake

Weighted Overlay

Malaria Vulnerability Map

Euclidean

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 Area Tabulate Area Tabulate Area Tabulate Area

Methods

Tabulation area analysis:

The generated outputs of Adama District malaria risk assessment were cross 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

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*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 3332.67 7.38 1462.71 6.99 2 Grass Land 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 665.73 5.71 2293.38 5.08

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 Office

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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 4059.87 5.22 736.07 0.95 2 Grass Land 2930.26 3.77 29.15 0.04 3 Cultivated Land 49.14 100

46554.29 59.89 21850.13 28.09

1136.51 1.46 0.19

0.00024

4 Water 97.33 0.13 336.07 0.43

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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.

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* 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!

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