Spatial Analysis of Malaria Hotspots in Dilla Sub-Watershed: Western Ethiopia

doi:10.21203/rs.3.rs-1526168/v1

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Spatial Analysis of Malaria Hotspots in Dilla Sub-Watershed: Western Ethiopia

https://doi.org/10.21203/rs.3.rs-1526168/v1

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The study was aimed to examine the spatial variations in malaria hotspots along Dilla sub-watershed in western Ethiopia based on environmental factors for the incidences; and compare risk level among districts and their respective kebeles. Ethiopian Central Statistical Agency based meteorological data, digital elevation model, soil and hydrological data were used. Different spatial analysis tools and Software were used for watershed delineation, generating malaria risk map for all variables, reclassification of factors, weighted overlay analysis and generation of risk maps. The findings of the study reveal that all districts and kebeles have different level of malaria risk due to variation in their environmental conditions. Accordingly, significant areas in Guliso, Aira, Boji Chokorsa, Boji Dirmaji, Jarso, Najo, Gawo Kebe, and Dale Wabera are characterized as high and moderately exposed to malaria risk. More specifically, about 14 kebeles of Aira, 12 kebeles of Guliso, 8 kebeles of Jarso, 8 kebeles of Najo, 4 kebeles of Boji Chokorsa, 4 kebeles of Boji Birmaji were highly and moderately exposed to the risk. In general, out of the total area of the watershed which accounts 2773 km², about 54.8% (1522km²) identified as high and moderate malaria risk area. The research output may help the government and humanitarian organizations to prioritize the interventions based on identified spatial variations in malaria severity. The research is aimed only for hotspot analysis which may not provide comprehensive account for community’s vulnerability to malaria. Thus, this research finding needs to be integrated with the socio-economic and other data for better malaria management in the area.

Malaria

hotspot analysis

risk level

Malaria is one of the world's most serious and complex public health problems, and it remains the greatest killer of human beings in developing countries (Aynalem, 2014). Malaria occurs in over 100 countries and more than 40% of the people in the world are at risk. The World Health Organization estimates that 300–500 million cases of malaria occur worldwide each year, with over two million people dying as a result. Malaria is a leading cause of morbidity and mortality in the developing world, especially in sub-Saharan Africa, where the transmission rates are highest and where it is considered to be a major impediment to economic development. The majority of malaria deaths occur in Africa, south of the Sahara, where malaria also presents major obstacles to social and economic development. It has been estimated to cost Africa more than US$ 12 billion every year in lost GDP, even though it could be controlled for a fraction of that sum(Qayum et al., 2013; Shargie et al., 2008).

The malaria epidemic and its far-reaching socio-economic impacts are significant, and cases are increasing at an alarming rate in sub-Saharan countries of Africa. However, malaria risk varies significantly from place to place and season to season due to weather and climate controls, primarily rainfall and temperature, which are further influenced by, among other things, altitude in Ethiopia (Aynalem, 2014). Topography made the ground fertile for the reproduction of the epidemic. It is estimated that three-fourths of the land and more than 68% of the population live below 2000 meters, with consequent variation in minimum and maximum temperatures, which is particularly malarious in Ethiopia. This makes malaria the number one health problem in the country, with an average of 5 million cases a year and 9.5 million cases per year between 2001 and 2005. The peak of incidence follows the main rainfall season in July, August, September, October, and November (Olana et al., 2011).

Though the impact of malaria derives from the natural environment that is favorable to its transmission, socio-economic variables are important determinants of the vulnerability. According to the Carter Center report, a lack of implementing the systematic approach of preventive plans because of the low socio-economic status of the society aggravated the incidents(Ayele et al., 2012). As a result, the cost of preventive plans and medical treatment becomes an enormous burden on the GDP. At the same time, the economic productivity of the individual has declined due to malaria infection. It has been estimated that the economic burden due to malaria accounts for a 1.3% reduction in the annual economic growth rate of Ethiopia. These conditions seek appropriate information regarding the hotspots of the malaria outbreak in order to effectively implement a control plan.

Spatio-temporal variability in bio-physical factors significantly determines malaria distribution in Ethiopia, while prevention and remedial measures have been taking place by the government and other stakeholders without due concern for these factors. In particular, the analysis of the spatial variability of these factors and how the conditions contributed to the distribution of the disease. Specifically, Wollega University's department of Social Work, in collaboration with Gimbi hospital, attempted to provide community service to enhance community engagement in controlling the epidemic in the West Wallagga zone, focusing on HIV/AIDS and malaria in the last five years. During the implementation of the project, the researcher, as the delegated person to facilitate the work, observed some of the challenges faced by the experts, mainly in screening the hot-spots of malaria to prioritize the areas of intervention. They were forced to use the hospitals' or health centers' case reports to select the areas of implementation. However, vulnerability to malaria doesn’t imply the level of the disease prevalence to implement prevention genuinely based on the risk. Rather, it is the function of socio-economic conditions. Therefore, identifying communities’ levels of exposure to malaria due to their geographical and biophysical positions has paramount importance in mitigating the breeding of mosquitoes and transmission of the disease.

Malaria breeding and transmission is associated with physical factors such as altitude, temperature, and rainfall, accessibility to river, lakes and swamps (Addis et al., 2011) which could have studied by GIS applications but still not widely applied in the country. A detailed understanding of what drives heterogeneities in the distribution of mosquitoes and mosquito-borne diseases could be analyzed by this tool which can help us to design better, more efficient control programs that maximize the use of limited resources(Gimnig et al., 2007).

Spatial technology has clearly defined the epidemiology of disease Vis environmental factors by identifying the spatial limits of the disease prevalence and risk mapping with relevant risk factors(Qayum et al., 2013). Geographic information systems (GIS) are powerful computer mapping and analysis systems for studying spatial patterns and processes; they are applicable to numerous disciplines, including the study of mosquito ecology. The distribution of mosquitoes could be studied by this technology depending upon the spatial distribution of their larval breeding sites, their flight range and the spatial distribution of their preferred hosts. These are all heterogeneous in space and time and GIS therefore has many potential applications to the study of mosquitoes and the diseases they transmit. It is used to map and analyze the spatial distribution of mosquitoes and to assess the ecological factors that contribute to observed distributions.

However, the potential application of GIS to epidemiological studies has been shown by only recent studies and very few applications have been made, even though epidemiological data clearly have spatial components(Devi et al., 2003). Never the less, GIS and remote sensing could be a fantastic tool for disease management as it has inherent ability to manage spatial, non-spatial and temporal data to investigate associations between environmental variables and the distribution of the different species responsible for malaria transmission(Qayum et al., 2013).

Moreover, the study watershed was selected with strong assumption that the area is one of the malaria hotspot in the country due to facilitating physical and socio-economic conditions. According to Aynalem, Oromia region is one of the most populous and malaria-prone region in Ethiopia where three-quarters of the administrative districts (242 out of 261) and 3932 kebeles out of 6107 are considered malarious. Seventeen million people are at risk in the region with annual clinical cases numbering between 1.5 and 2 million(Aynalem, 2014). Turner, 1972 has identified some districts in the region as the priority area for malaria investigation in his report. This is the ground for purposive selection of the watershed for this project work. The watershed encompasses eight districts of west Wallagga zone and four districts of Qellem Wallagga. The physical setting of the study area highly facilitates the malaria parasite breeding with dominant altitude less than 2000 meter (Altitude ranges from 1259–2499) adequate rainfalls (ranges from 1292 to 1738mm) and average annual temperature ranges from 19-20⁰c. Therefore, identifying and mapping of malaria prone area and characterizing the areas according to their risk level in the watershed have extra-ordinary importance in prioritizing health facility, controlling and monitoring the disease. Likewise, the identification of level of incidents could enhance the effectiveness of prevention efforts and will substantially reduce costs of prevention with efficiently targeting of high risk areas.

The objective of the study is to generate malaria risk map of Dilla watershed based on environmental factors responsible for malaria prevalence and compare malaria risk level among the districts and their respective kebeles; so as to recommend the priority areas for controlling and monitoring activities by concerned body.

The audiences for this study will be from both academic and non-academic area which will benefit all of these audiences for various purposes. The research will fill the gap on identifying malaria risky areas and vulnerability analysis which traditionally take place based on health centers reports. Moreover, this research work will contribute to identify malaria hot-spots for the community service project intended by the University in the last three years because Dilla watershed for which this research work is proposed covers about 12 districts of West and Qellem Wallagga zones; covering the largest part of Wollega University’s research and community service geographical interests.

The hotspot of malaria is governed by a large number of factors relating to the parasite, the vector and the socio-economic condition of the communities. Predominant among these are physical factors affecting habitat and breeding sites of the Anopheles mosquito vectors such as temperature, precipitation, humidity, presence of stagnant water pools, vegetation which are related with other factors such as elevation and slope of the area. Specifically, temperature and rainfall act as limiting factors on the development of Anopheles mosquitoes, which are the intermediate hosts in the transmission of malaria parasites. About 70–90% malaria risk is associated with environmental factors, which in turn influence the abundance and survival of the vectors(Saxena et al., 2009).

In this study, the physical factors identified in various researches to significantly contribute to the effects of malaria distribution are identified with great care and by critical analysis of studies conducted in Ethiopia. The standard to determine and rate the effect of each factor is also based on this literature (Aster & Seleshi, 2009; Aynalem, 2014; Olana et al., 2011). These factors are described below:

Temperature

Temperature is one of the key environmental contributors to mosquito propagation. High temperature speeds up the development of the life cycle of a mosquito and accelerates the length of the development of the life cycle of malaria parasite within the mosquito host. The optimum temperature for development of malaria parasite is between 25°C to 30°C. At lower temperature less than 16°C, the larval and pupal stages of mosquitoes take longer time to complete sporogony cycle and below 16°C the sporogony ceases. However, with an increase in temperature sporogonic period become shortened and the Plasmodium parasite within the vector increase and effective up to about 30°C. Contrary to this, increased temperature above 30°C has negative impact on the survival of the vector(Mestewat, 2014).

In general, the temperature less than 16°C and above 30°C, are less conducive for malaria breeding. Therefore, based on these literatures the temperature based malaria hot spot of the study area classified as high and very high for 19-21⁰C and 21-24⁰C respectively; believing that the temperature increasing more than 25⁰C will gradually decreases the breeding (The detail of the standard used is described in the Table 1).

Rainfall

The association between rainfall and malaria epidemics has been recognized in various literatures(Mestewat, 2014). But it can be seen in two ways. First, increasing precipitation may increase vector populations in many circumstances by increasing available anopheles breeding sites. Excessive rainfall in warm, arid areas can lead to increased transmission due to creation of vector breeding sites. Apart from creating mosquito breeding sites, rainfall also affects malaria transmission through increasing humidity, which in turn will help to increase the longevity of the adult vectors Second, excessive rains may also have the opposite effect by flushing out small breeding sites, such as ditches or pools or by decreasing the temperature, which in regions of higher altitude can stop malaria transmission.

Based on this information, it is possible to identify the significant amount of rainfall support malaria breeding while other physical factors are highly associated with its effectiveness. Areas with annual rainfall amount greater than 1000 mm are malarious and have intense malaria transmission, but areas with rainfall amount between 500 and 1000 mm have seasonal transmission (President’s Malaria Initiative, 2012).Moreover, very high rainfall is not suitable for vector immature stages, areas having > 1600 mm annual rainfall is unfavorable for mosquito breeding. In general, rainfall based mosquito breeding zones are classified as high and very high for rainfall amount of 1504–1600 mm and > 1600 mm respectively.

Elevation

Malariologists working in the field in the first half of this century, in the decades following the elucidation of the malaria cycle in man and mosquitoes, appreciated that it was a focal disease and that the topography of the land was an important consideration in understanding the local epidemiological situation(Qayum et al., 2013). Topography (particularly altitude and slope) are identified by (Saxena et al., 2009) as an important factor in understanding the malaria epidemiological situation at local scale.

It is locally well-known that the prevalence of malaria parasites in people varies with altitude. People at low lands have significantly higher prevalence of malaria than those in middle and highlands. Therefore, altitude is significant in determining the distribution of malaria and its seasonal impact on many parts of the World. Based on altitude, traditionally Ethiopia is divided in five agro-ecological climatic zones. These are locally known as Baddaa Dilallaa, Baddaa, Badda Daree, Gammojjii and Gammoojjii Ho’aa. In Ethiopia the both Baddaa Dilallaa and Baddaa zone with elevation above 2500 meter above mean sea level is malaria free even though the dynamics is high due to present climate change and variability. However, malaria frequently occurs in areas below 2000 meters elevation and the transmission is very intense in areas below 1500 meters elevation (Aynalem, 2014). Particularly, in some parts of Badda Daree, and in most parts of Gammojjii. Contrary to this, the areas of Gammoojjii Ho’aa have an altitude < 500 meter above mean sea level and annual rainfall of < 900mm is not suitable for mosquito breeding as a result of low annual rainfall amount and very high temperature above 300C.Therefore, the study area is classified in to three classes as 1317- 1500masl, 1500–2000 m and > 2000 meter for very high, high and low, respectively.

Slope

Malaria breeding is affected by slope of the land. Mosquito larvae need stagnant water pools to survive, and these pools are less likely to form in steep slope areas. Moreover, larvae developing in water pools in sloped areas are more likely to be washed away during downpours. Thus, sloped areas make poor mosquito breeding ground, reducing the threat of malaria transmission because of low water stagnation. In general, the steeply the slope, the low mosquito prevalence and vice versa are well-known. Therefore, the slope classes 0–5%, 5–8%, 8–15%, 15–30% and > 30% are assigned for very high, high, moderate, low and very low slope based risk of malaria respectively.

Proximity to Water Bodies

Surface water provides the habitat for the juvenile stages (egg, larvae, and pupae) of malaria vectors. The state of small water bodies and wetlands is very useful source of malaria vectors. Seasonal variation in volume of water in Ethiopia results the variation in malaria breeding situation spatially and temporally. The rainy season for both the rivers and lakes lead to flooding which hinders vectors of malaria. However, during dry season their volume is decreased and they create different pockets of water body that is favorable ground for the breeding of mosquito. Therefore, even though the effect is varying seasonally, accessibility to water bodies aggravate the prevalence of malaria. The water body selected for this project could be river, lake and swamps. Accordingly, the value is assigned for these variables: Areas found < 1 km, 1–2 km and above 2–5 km from rivers and canals assigned as very high, high and low malaria risk level respectively.

Soil Moisture Holding Capacity

Soil moisture holding capacity/permeability is important factor determine mosquito breeding. Poorly drained soils is believed to facilitate water stagnation and create conducive conditions for mosquito breeding and thus, favorable for malaria outbreak. Well drained soil doesn’t allow water stagnation, so it creates unfavorable condition for anopheles breeding. Soil drainage classification of Ethiopia made by CSA, were used for this project. Accordingly, it is classified in to very poor, poor, moderately well-drained are assigned as very high, high and low malaria risk factor respectively.

3.1 Description of the Study Watershed

Dilla is a sub-watershed of Dabbus, which is one of the significant watersheds of the Blue Nile in the South Western and Western parts of the country. It is the largest river sub-basin whose watershed covers 12 districts out of 22 in the Dabbus Sub-basin of the Blue Nile Basin (Fig. 1). Astronomically, the watershed lies between 9⁰03'N -9⁰11'N and 34⁰36' − 35055'E which consists of eight districts of the West Wallaga zone, namely Babo Gambel, Jarso, Aira, Guliso, Yubdo, Boji Chokorsa, Boji Dirmaji, and Najo districts, and four districts of the Qellem Wallaga Zone, namely Gawo Kebe, Dale Wabera, Dale Sadi, and Lalo Kile.

The watershed has total areas of 2773 Km². The altitude in this watershed ranges approximately between 1317 masl and 2405 masl. The highlands in the north-eastern part of the sub basin are higher in altitude, greater than 1800masl whereas the western part has relatively lower altitude up to 1317 masl. The rainfall distribution is associated with altitude that the areas of high altitude in north-eastern and eastern part of the watershed experiences relatively highest rainfall up to 1723mm whereas temperature distribution is generalized as 19⁰c based on available data of CSA, 2007.

Accordingly, the watershed is characterized by hot to warm moist and sub humid lowlands and, the eastern and southern highlands the being tepid to cool sub humid mid highlands(Aster & Seleshi, 2009). The most dominant soil in the watershed is Alisols and Nitosols, with the occurrence of Vertisols, Fluvisiols, and Acrisols.

3.2 Research Design

Descriptive survey design was used in this study. Central Statistical Authority based data were obtained to identify factors determine malaria distributions in the watershed. Among others, the pertinent environmental factors determining mosquito breeding and malaria transmission were identified for the study (Aster & Seleshi, 2009; Ayele et al., 2012; Shargie et al., 2008). Accordingly, elevation, temperature, slope, soil drainage, rainfall and proximity to water bodies were selected. Malaria risk map were produced for all variables separately. Finally, all maps were overlaid to produce malaria risk area and used to compare the risk level of districts in general and their kebeles in particular in the study watershed. Hospital and health center data were collected to substantiate biophysical data.

3.3 Sources and Types of Data

The CSA based meteorological data, digital elevation model, soil data and hydrological data were used to derive rainfall, temperature, elevation, slope, soil drainage and proximity to water body of the watershed to produce temperature based malaria risk map, rainfall based malaria risk map, altitude based malaria hotspot map, slope based malaria risk map, soil drainage based malaria risk map and proximity to water body based malaria risk map. Hospitals and health center data from different districts was collected to check and substantiate biophysical data.

3.4 Description of Variables and Assigned Value

As described in section 2, six biophysical factors (independent variables) are identified for analyzing and mapping of malaria hot spots (dependent variable) in the study watershed. Thus, Malaria Risk = function (the impact of rainfall, temperature, altitude, slope, soil drainage and proximity to river based malaria risks). The value of each variable was assigned according to predetermined standard derived from the studies conducted in Ethiopia with great caution and experiences of the study area (Table 1).

Table 1

Standardized Value Assigned for Physical Factors for Malaria Risk Level
S.N	Factors	Unit	Risk Level
S.N	Factors	Unit	R1	R2	R3	R4	R5
1	Temperature	ᵒC				19–21	21–24
2	Rainfall	Mm				1504–1600	> 1600
3	Elevation	M			> 2000	1500–2000	1317–1500
4	Slope	%	> 30	15–30	8–15	5–8	0–5
5	Soil drainage	Water Drainage			Moderate	Poor	Very poor
6	Proximity to River	Km			2–5	1–2	< 1
R1, R2, R3, R4 and R5 represent very low, low, moderate, high and very high level, respectively

Sources: Modified from (Aster & Seleshi, 2009; Hanafi-Bojd et al., 2012; Mestewat, 2014)

3.5 Methods of Data Analysis

Arc SWAT for watershed delineation, and Arc GIS 10.3 was used for generating malaria risk map for all variables, reclassification of factors, for weighted overlay analysis and generation of risk maps through the application of spatial analysis tools. In this study, the analytical hierarchical process (AHP) was applied. It is a multi-criteria decision method that represents an issue using hierarchical structures and makes judgments based on expert input to determine priority scales. The mapping weight or relevance of each individual malaria risk factor was determined using AHP in this study(Chikodzi, 2013). To determine the weights of each factor, pairwise comparison matrix was created for each of the input parameters, and then the relative weights of each input parameter were determined.

Weighting of the impact of each variable on malaria distribution is one of the important issues in malaria hotspot analysis because all variables may not affect the distribution equally. In this regards, similar to most research conducted in Ethiopia, the hospitals report were used to substantiate the decision on the weight for the factors (Table 2). So the rating or assigning weights for the factors depends on previous researches, knowledge of the study area and hospitals/health center data on records of malaria patients(Ahmed, 2014; Gimnig et al., 2007; Mestewat, 2014).

Table 2

Weight Assigned for the Factors
S.N	Factors	Assigned Weight /100%
1	Temperature	25
2	Rainfall	20
3	Elevation	22
4	Slope	11
5	Soil drainage	10
6	Proximity to River	12

Malaria sensitivity of the study area classified for all variables is presented in map separately based on value assigned in the above table (Table 1). The map of each variable which were classified according to predetermined value was reclassified by spatial analysis tool to facilitate weighted overlie analysis. Because each variable have its own unit of measurement which should be converted to integer value (1 to 5) so as to enable us the overlying activity. Finally, the map displaying the integrated effects of all variables was produced based on weight assigned for the factors out of 100% (Table 2).

Finally, the malaria risk map were integrated with the shape file of the districts and their respective kebeles to produce the watershed’s malaria risk map showing susceptibility level as high and moderate risk. Moreover, tables were also used to describe the map depictions.

The following flow chart is used to show the general procedures applied in the project work.

4.1 Physical Factors Based Malaria Hot Spot of the study Area

Based on the value assigned, the amount and distribution of rainfall experienced in the watershed shows spatial variations for malaria breeding suitability. Therefore, the ideal place for rainfall based mosquito breeding is the area lies within 1601-1723mm of rainfall which assigned as the highest rainfall based malaria risk area followed by the rainfall 1504-1600mm. There is no place with low and very low capacity for malaria breeding. In general, as indicated on the map below (Fig. 2A), 72.7% of total watershed lays highest rainfall based malaria risk area while the left 27.3% are within moderately high risk area (Table 3).

The area within altitude of 1317–1500 masl was assigned as the highest elevation based malaria hotspot followed by 1501–2000 and > 2000 as high and moderate respectively. As altitude increase or decrease from this standard its suitability for malaria breeding decreases. Thus, these classifications were made to show areas with very high, high and moderate in altitude based malaria risks of the watershed (Fig. 2B). About 30.9%, 68.7% and 0.4% of the watershed lies within very high, high and moderate elevation based malaria risk (Table 3).

Temperature is one of the determinant factors facilitating malaria breeding in the study area. According to criteria sated in different literature, the temperature between 21-24⁰c is the most suitable for malaria survival followed by 19-20⁰c which assigned as high. However, according to data on hand, the region lies totally within 19⁰c (Fig. 2C). Therefore, it is possible to conclude that the watershed has high vulnerability to temperature based malaria.

Slope of the study area were broadly categorized in to five as 0–5, 5–8, 8–15, 15–30 and above 30 by which the values were assigned as very high, high, moderate, low and very low respectively according to their ability to facilitate mosquitos breeding. Therefore, as indicated below on (Fig. 3A), 43.2% of the watershed lies within 0–5 slope that the area is characterized as very high slope based malaria risk area. Similarly, 31.6%, 22.7%, 2.4% and 0.1% are believed to be high, moderate, low and very low slope based malaria risk area. This indicated that the slope of the watershed is very suspeciable for mosquito breeding.

Soil water holding capacity determines mosquitos’ breeding. Therefore, soils with greater water retention capacity facilitate malaria prevalence in the study area. Accordingly, highest percentage (96.5%) of the watershed is characterized by moderately well-drained soil types while only 2.1% and 1.4% are poor and very poor in soil drainage respectively(Table 3). Therefore, the soil drainage based malaria risk of the study area is relatively low when compared with other variables (Fig. 3B).

Proximity to water body has direct implication on vulnerability of a given community to malaria hazards. The region closer to water body and swampy area has greater malaria breeding and flying zone risk. Thus, based on the criteria already identified, the study watershed has classified to closest region was assigned with 1kilometer buffering from the river while 1–2 kilometer and 2–5 kilometer are assigned as moderate and low respectively (Fig. 3C). Accordingly, only 59.5% of total areas of watershed lies within the buffering of 5Km which are subjected to malaria risk of different levels. Thus, out of the total malaria vulnerable area (1655 Km²) the region most vulnerable due to its closest distance to the rivers (< 1 Km) account 24.4% followed by medium distance (1–2 Km) accounts 22% and relatively far from the rivers (2–5 Km) which account for 53.6% (Table 3).

Table 3

Summary of the Malaria Risk Level along the Variables
S.N	Variables	Risk Level	Area(Km²)	Percentage
1	Rainfall	Very High	2016	72.7
		High	757	27.3
		Total	2773	100
2	Altitude	Very High	856.9	30.9
		High	1905	68.7
		Moderate	11.1	0.4
		Total	2773	100
3	Temperature	High	2773	100
4	Slope	Very High	1197.9	43.2
		High	876.3	31.6
		Moderate	629.5	22.7
		Low	66.5	2.4
		Very low	2.8	0.1
		Total	2773	100
5	Soil Drainage	Very poor	58.2	2.1
		Poor	38.8	1.4
		Moderate	2676	96.5
		Total	2773	100
6	Proximity to Rivers	Very High	403.8	24.4
		High	364.1	22
		Moderate	887.1	53.6
		Total	1650	100

4.2 Malaria Distributions and Risk level of the Watershed

Malaria risk map is the result of the overlaid maps of all variables according to their impact factors (assigned weight). Administrative map of the watershed were also integrated to the risk map to identify the level of districts and their respective kebeles risk to malaria hazards which helps the decision makers to identify areas requiring greater priority to health facility in the study area. As it was explained under the description of the study area, the watershed consists of 12 districts which eight of them are in west Wallaga zone and four in Qellem Wallaga zone (Table 4).

The malaria risk map of the watershed indicated that all districts and kebeles have different level of malaria risk due to variation in their physical settings affecting mosquito breeding. Accordingly, significant numbers of kebeles in Guliso, Aira, Boji Chokorsa, Boji Dirmaji, Jarso, Najo, Gawo Kebe, Dale Wabera and others are high and moderately affected by malaria risk (Fig. 4). Moreover, it is possible to prioritize the districts highly affecting by malaria depending on numbers of kebeles lies within high and moderate risk level (Table 4).

In general, out of the total area of the watershed which accounts 2773 km², 54.8% (1522km²) identified as high and moderate malaria risk area. The details of districts and kebeles risk level are described in the Table 3 below.

Table 4

Malaria Risk Level of Dilla Watershed in districts and kebele
S.N	Districts	Risk Level	List of Kebele
1	Aira	High	Kure, LaloSuchi, WarababoSuchi, WarakuraSuchi, Norther part of WarawayuGerjo,WayuManni Sanki, HomiSuchi, Northern part of DagagaAira, Western part of Katta Abba Korma, TegiGallawo
1	Aira	Moderate	Centeral part of Kure and Northern Lalosuchi
2	Guliso	High	GawoGanka, Bedas Dilla, HawateSuchi,SekaJirbi,Maru, Sanki, ChaliyaKusaye, ChaliyaWaraDalle,JarsoBadeso, JarsoLalo,
2	Guliso	Moderate	Some parts of Bedas Dilla, GawoGanka
3	BaboGambel	High	Ambalo Dilla, southern half of WarajiruBako
3	BaboGambel	Moderate	MalkaEbicha
4	Jarso	High	Abono Dilla, GedoArgame,SouthernTuquSadan, south of GebaDafino,HidebuNyaha, BaboTiruben and Bedas Dilla
4	Jarso	Moderate	Centeral part of GedoArgame and TuquSadan
5	Najo	High	Gunde Mikael, Didis Dilla, Gute Dilla, AmumaDilla,GamtaAmuma
5	Najo	Moderate	HomiGormitu, Lalistu Dilla, GidaKumbi
6	BojiChokorsa	High	Waligalte Dilla, TulluGuracha, western tip of Abo Makajarte, Western half of ChaliyaWara Ilu
6	BojiChokorsa	Moderate
7	BojiBirmaji	High	Eastern half of Bikiltu Dilla, AmomaBore,AgaloSilkan, west of AmumaAgelo
7	BojiBirmaji	Moderate	North of DidibeTuli, east of AgaloSilkan, western half of Bikiltu Dilla
8	Yubdo	High	Some Kebeles of Yubdo woreda found at south eastern tip of the watershed is characterized by high altitude. Thus no Kebeles in the woreda has identified as high and moderate risk of malaria.
8	Yubdo	Moderate
9	GawoQebe	High	North-east of Sichawo, North of Bata Dale and northern tip of BiftuChiro
9	GawoQebe	Moderate	Northern part of BiftuChiro and Sichawo, eastern part of central Habro, centeral Bata Dale
10	Dale Wabera	High	Northern part of Haroji Hobo and AdanoWal-bata
10	Dale Wabera	Moderate	Scattered part of Haroji Hobo and AdanoWal-bata
11	Dale Sadi	High	Kebeles of Dale Sadi woreda found at southern tip of the watershed is characterized by high altitude. Thus no Kebeles in the woreda has identified as high and moderate risk of malaria.
11	Dale Sadi	Moderate
12	LaloQile	High	Sarba Rajo is the only kebele found in this watershed with no high and moderate malaria risk level
12	LaloQile	Moderate

Biophysical parameters identified in this study were found to have effects for the spatial variation in malaria prevalence, which corroborated prior studies done in different parts of Ethiopia and other regions of the world that came to similar results (Chikodzi, 2013; MOH, 2020). The study found that the western and southwestern areas of the watershed had the highest malaria risk, whereas the risk decreased in the eastern part due to biophysical variations. The historical clinical data of malaria incidences by district, and other malaria spread models confirm a similar pattern in the spatial epidemiology of malaria. Specifically, the study has also shown that the risk of malaria in Dilla watershed declines with increasing altitude; while altitude is the most determinant factor for spatial variation in other biophysical factors. Other researchers also noted that as one goes up in altitude, the transmission potential falls off tremendously (Aynalem, 2014; Gimnig et al., 2007; Olana et al., 2011).

The significant difference between the result of spatial hotspot analysis and clinical data of malaria incidences caution us to consider vulnerability analysis to pinpoint the real and pressing factors for the effects on malaria on the local community(Hanafi-Bojd et al., 2012; Saxena et al., 2009). It has been suggested that this testimony is not the only mechanism to identify the spatial situation of malaria risks. Rather, the malaria risk maps ought to be produced by considering other socioeconomic and demographic factors are very relevant. Although such hotspot analysis are important, malaria vulnerability analysis become resonant issue than before. Second, future studies need to uses greater, sharper and finer spatial and temporal resolutions of biophysical risk factors. Thirdly, the physical factors such as climate and geomorphologic variability as a natural variable should be considered for their dynamism, that impacts on the conditions that affect mosquito proliferation. Moreover, this dynamism also includes human induced variables like land cover/ land use changes.

From the study case, it can be inferred that there is a wide spectrum of possibilities over which GIS can be attributed to malaria control measures. Its application as an operational planning aid is an extension of geographical reconnaissance to promote better program management at both the local and national levels.

True to the objective of the project, malaria risk levels of the watershed were identified at district and kebele level. Physical factors used to generate malaria risk map of the study area include temperature, rainfall, elevation, slope, soil drainage and proximity to rivers. Maps were generated for all environmental factors and then reclassified based on their suitability for mosquito breeding and malaria incidence.

The final map of malaria risk level of the watershed indicates that 54.9 percent of total area of the watershed lies within high and moderate malaria risk level. This is estimated to be 1522km² of land area. Out of these total area, 82.2% lie within high malaria risk while the rest 17.8% is within moderate risk level.

At district level, significant areas of Aira, Guliso, Boji Birmaji, Boji Cokorsa, Najo and Jarso are found in high and moderate malaria risk level. More specifically, about 14 kebeles of Aira woreda, 12 kebeles of Guliso districts, 8 kebeles of Jarso districts, 8 kebeles of Najo districts, 4 kebeles of BojiChokorsa districts, 4 kebeles of Boji Birmaji districts and others are highly and moderately affecting by malaria prevalence.

The health office of West Wallega and Qellem Wallega zones are required to prioritize the areas of interventions based on the risk level at districts and kebele to provide malaria drug, bed net distribution and house spraying. This study was aimed only for malaria hotspot analysis. However, the map generated can be integrated with socio-economic and other data to provide excellent model for the malaria disease management.

Funding

No fund was provided to design the study, collection, analysis, and interpretation of data, and in support to write the manuscript.

Availability of data and materials

Datasets used in this study are available from the corresponding author on reasonable request.

Contributions

The biophysical data used in this study were prepared by the Ethiopian Central Statistical Agency. Data interpretations, analysis and manuscript drafting was done by the author.

Consent for publication

Not applicable.

Competing interests

There is no competing interest.

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No competing interests reported.

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Spatial Analysis of Malaria Hotspots in Dilla Sub-Watershed: Western Ethiopia

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Abstract

Figures

1. Introduction

2. Conceptual Framework Of The Study

3. Methods And Materials

3.1 Description of the Study Watershed

3.2 Research Design

3.3 Sources and Types of Data

3.4 Description of Variables and Assigned Value

3.5 Methods of Data Analysis

4. Results

4.1 Physical Factors Based Malaria Hot Spot of the study Area

4.2 Malaria Distributions and Risk level of the Watershed

5. Discussions

6. Conclusions

Declarations

References

Additional Declarations

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