MALARIA RISK ASSESSMENT USING GEOGRAPHIC INFORMATION SYSTEM (GIS):

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1 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, /30/2016 3:36:41 PM 1

A short Professional chronicle of Kibrom Hailu Selected publication Kibrom Hailu T., Tilahun E, and Daniel A,. GIS based Malaria Risk Assessment A case Study of Adama District, Ethiopia, 2012.

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.

2 A short Professional chronicle of Kibrom Hailu Selected publication Kibrom Hailu T., Tilahun E, and Daniel A,. GIS based Malaria Risk Assessment A case Study of Adama District, Ethiopia, LAP LAMBERT Academic Publishing. ISBN: Deutschland / Germany. Kibrom Hailu T., Nesru M. Evaluation of Solid Waste Dumping Site using GIS & MCE A case study of Addis Ababa, Ethiopia, LAP LAMBERT Academic Publishing. ISBN: 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, Safe removal of Solid Waste using Re-engineered design of inclination of chimney for Adama Science and Technology University, Ethiopia, 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 9/30/2016 and 3:36:41 Geographic PM Information System (GIS) 2

3 Presentation out line Introduction Methods Results and Discussions Conclusions and Recommendations 9/30/2016 3:36:41 PM 3

4 Definitions and Options 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

5 Definitions Cont d 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 /30/2016 3:36:41 PM 6

6 Introduction 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. 9/30/2016 3:36:41 PM 7

7 Introduction Cont d 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 9/30/2016 3:36:41 PM 8

8 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. 9/30/2016 3:36:41 PM 9

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? 9/30/2016 3:36:41 PM 10

10 Introduction Cont d 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. 9/30/2016 3:36:41 PM 11

Study area General Description Study area: Adama District Location (511737.875, 910052.511)min (546432.813, 964326.

11 Study area General Description Study area: Adama District Location ( , )min ( , )max Altitude: 1500 to 2300m a.m.s.l Area is 77779ha. Mean annual rainfall of 740mm. Temperature ranges 10 o c to 32 o c. 9/30/2016 3:36:41 PM 12

12 Methods 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 = MH x V x Er *MR = Malaria Risk *MH = Malaria Hazard, *V = Vulnerability, *Er = Element at risk 9/30/2016 3:36:41 PM 13

13 DEM AEZ Soil River Slope Slope AEZ Rasterization Soil River Schematic representation of malaria risk analysis Reclassification Euclidean Distance Slope AEZ Soil River Weighted Overlay Reclassification Malaria Risk Map Weighted Overlay Malaria Hazard Map Malaria Vulnerability Map Element at Risk Reclassification Weighted Overlay River Pop. Density 9/30/2016 3:36:41 PM 14 WI LU Lake Reclassification IDW DEM WI LU Lake Lake Euclidean Population

14 Methods Tabulation area analysis: The generated outputs of Adama District malaria risk assessment were cross tabulated with Kebele & LU for further investigations. Tabulate Area Land Use Tabulate Area Malaria Risk Risk Vs LU Tabulate Area Hazard Vs LU Risk Vs Kebele Malaria Hazard Hazard Vs Kebele Kebele Tabulate Area 9/30/2016 3:36:41 PM 15 *LU = Land Use, *Kebele = small administration unit * Vs = inter comparison

15 Results and Discussions 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 % 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. 9/30/2016 3:36:41 PM *AEZ = Agro Ecology Zone 16

16 Results Cont d Malaria Hazard Assessment The Consistency Ratio was 0.07, Which is acceptable as the values <= 0.1 W AEZ =54% W SLOPE =28% Ranks of Hazard Area (ha) Hazard level (%) Low Moderate W SOIL =12% High Very High /30/2016 3:36:41 PM Total Area W RD =6%

17 Results Cont d 2. Malaria Risk Assessment Low malaria risk areas (68.97% ) are highly influenced by elements at risk (due to less population density) factor level % of the area is rated as moderate to very high risk areas. due to the associated hazard & element at risk level. 9/30/2016 3:36:41 PM 18

18 Results Cont d Malaria Risk Assessment All factors have equal weight of importance Ranks of Risk Area (ha) Risk (%) Very Low Low Moderate High Very High /30/2016 3:36:41 PM 19 Total Area

19 Results Cont d 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. 9/30/2016 3:36:41 PM 20

20 Results Cont d Id Land Use Low Moderate High Very High Area (ha) % Area (ha) % Area (ha) % Area (ha) % Shrub Land Grass Land Cultivated Land Water Hazard Vs Land Use 9/30/2016 3:36:41 PM 21

21 Results Cont d 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 9/30/2016 3:36:41 PM 22

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

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

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 Grass Land Cultivated 3 Land Water Malaria Risk Vs Land Use 9/30/2016 3:36:41 PM 25

25 Results Cont d 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. 9/30/2016 3:36:41 PM 26

26 Result Validation of Malaria Risk Assessment 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 * PMRMSE =Residual Mean Square Error, * GPS = Global Positioning System 27

27 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 which is positive and strong relation between the two data. 9/30/2016 3:36:41 PM 28

28 Conclusions and Recommendations 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 nonhazardous 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. 9/30/2016 3:36:41 PM 29

29 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

30 Limitation of the study 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. *AEZ=Agro-Ecology Zone, * Adama District Health Office 9/30/2016 3:36:41 PM 31

31 FOREVER ETHIOPIA, AFRICA!! Ethio-SAT, 2035, Innovation make a difference! 9/30/2016 3:36:41 PM 32

ICES Geospatial Engineering Core Competencies Final GEOSPATIAL ENGINEERING COMPETENCIES. Core Geospatial Competencies

ICES Geospatial Engineering Core Competencies Final GEOSPATIAL ENGINEERING COMPETENCIES. Core Geospatial Competencies GEOSPATIAL ENGINEERING COMPETENCIES Core Geospatial Competencies GECORE01 Competency Project Specification and Brief A All E All B 1. Analysis of client requirements 2. Development / interpretation of