Malaria represents one of the largest problems threatening global public health [1] with total cases exceeding 212 million worldwide in 2015, of which 90% were in Africa. Moreover, Malaria caused 429 thousand deaths globally in 2015 [2] . In this respect, it was estimate that Malaria was the seventh leading cause of death in low-income countries by 2015, leading to 34.5 causes of deaths per 100,000 of population on average in these countries [3] . As one of vector-borne disease systems, malaria is caused by Plasmodium parasites that transmitted by Anopheles mosquito. Malaria transmission and distribution are generally determined by a wide range of climatic, topographic and socioeconomic factors [4] [5] . While both climatic and topographic factors determine the suitable habitats for Plasmodium and Anopheles, socioeconomic factors are more relevant to adaptive capacities of individuals or communities and consequently determine their vulnerability to malaria.

A wide range of research work was undertaken to recognize the various factors underlying malaria risk in different countries [6] . The contribution of climate in malaria transmission was already recognized [7] and the association between epidemics of malaria and climate was evidenced in different regions worldwide in particular in sub-Saharan African countries [1] [8] [9] , Latin America [10] and Asia [11] . Generally, malaria incidence increases with warm and wet conditions, where higher temperature leads to increasing malaria incidence as it contributes to shortening extrinsic incubation period of Anopheles mosquito [8] [12] . Generally, it was suggested that the optimum temperature for Anopheles mosquito and parasite development ranges between 20˚C and 30˚C and they are subjected to thermal death if temperature rises above 40˚C or drops below 16˚C [13] . Yet, it is worth mentioning that the impact of temperature on malaria cannot be fully explained only by mean temperature [14] . Actually, diurnal temperature range has significant impacts on malaria transmission, where more diurnal temperature range in relatively cold conditions, with mean temperature below 21˚C, may increase malaria transmission as it accelerates mosquito development. Meanwhile, more diurnal temperature range in warmer conditions, with mean temperature above 21˚C, may lead usually to decrease malaria transmission as it may slow-down mosquito development [14] [15] . Also, malaria incidence strongly associated with wet conditions, higher levels of rainfall [8] or relative humidity [4] that usually support Anopheles mosquito to complete their life cycle [10] . In this respect, 80 mm per month of rainfall for at least five months was suggested as a threshold limit that is sufficient to malaria transmission [4] .

Moreover, malaria incidence is significantly affected by some topographic factors such as altitude, slope and land cover. In this respect, the negative relationship between the risk of malaria infection and altitude was well-recognized. For instance, it was argued that malaria morbidity was significantly less prevalent in the highlands that experience relatively cold conditions compared to lowlands [16] . Such low temperature in high altitude limits parasite development and consequently restricts the spread of malaria there [12] . Similarly, malaria risk is negatively associated with slope, where breeding sites of mosquitos are more likely to be in gentle slopes and planes [17] . Usually, katabatic wind in steep slopes and a relatively cold temperature during nights restrict dispersion of mosquitos into higher altitudes [17] [18] . Also, malaria risk increases in some patterns of land cover that are dominated by wetlands, farmlands and regularly flooded land. Such type of land covers provides suitable sites for Anopheles mosquitos to complete their life cycle [10] [12] [19] .

As a result of sensitivity of malaria transmission to climate and topography, malaria was defined as a highly climate sensitive disease [10] and an environmental related disease [20] . Such sensitivity, also, implies that the great potentials for mapping malaria risk through various climatic and topographic variables that determine malaria transmission. Recently, mapping malaria has been promoted by increasing data availability, improved insight into environmental covariates of malaria risk, advances in analytical techniques [21] and availability of affordable Geographical Information Systems (GIS) software [4] . The application of GIS was not restricted only to mapping malaria incidence but extended also to include assessing the relationship between malaria and underlying factors, developing malaria early warning systems [13] and planning for malaria control programs [22] . This was motivated by the nature of data on malaria incidence and underlying climatic, topographic and socioeconomic factors are spatial variables that can be modeled and manipulated through GIS due the great capabilities of such systems in terms of handling and analyzing spatial data. In this respect, it is worth mentioning that the successful application of GIS in mapping malaria and planning for control programs depends largely on availability of accurate and updated data on malaria cases [22] .

Malaria is one of the main health issues in Burundi in terms of morbidity, mortality and associated sever economic costs [8] . In 2015, Burundi had 2 million cases of malaria, which led to about 5600 deaths [2] . Due to its significant magnitude, malaria in Burundi was considered repeatedly. For example, Nkurunziza et al. (2010) and Nkurunziza et al. (2011) attempted to provide better understanding on various influencing climatic factors [6] [8] . More recently, Moise et al. (2016) examined the spatial patterns of malaria morbidity [23] . It was projected that climate change will have significant impacts on expanding spatial extent of the areas vulnerable to malaria risk [24] [25] as a result of expected warming and increasing humidity [26] . However, none of the previous research work considered the potential impacts of climate change on malaria incidence in Burundi. This, in turn, emphasizes the need for developing a methodology for mapping malaria risk and delineating those regions that may potentially experience malaria epidemics under climate change in the future.

The paper in hand is intended to map malaria risk in Burundi under climate change up to 2050. For this purpose, a GIS-based model will be developed for mapping malaria through its climatic and topographic determinants. The spatial variations of the considered determinants are employed to predict those areas of high malaria risk under expected climate change. Such a prediction is thought that, of great importance due to its role in improving resilience of the country to climate change associated risks.

Burundi is one of the sub-Saharan African countries that is located between 2˚20' and 4˚27' South and between 28˚50' and 30˚53' East [8] . Covering a total area of 27,834 km², of which 8% are water surface occupied by Lake Tanganyika; the second largest freshwater lake in the world [27] . The country is one of the most populated countries in East Africa with estimated total population size of 9.8 million inhabitants in 2015. This population primarily lives in rural environments, where urban population represents only about 15% of the total population [28] . The country is administratively subdivided into 18 provinces (Figure 1), which are subdivided into 46 health districts [29] .

Burundi has a mountainous surface with altitude ranging between 775 meters and 2670 meters above mean sea level [8] . The country is dominated by high plateau in the center with an average altitude of 1500 meter above mean sea level [27] (Figure 2). Also, Burundi territory has some plains including Imbo in the West, the Kumoso depression in the East [30] . The varied altitude has considerable impact on temperature, where the highest mountain areas have an average annual temperature of 16˚C, while the central plateau has cool temperature with annual average temperature of 20˚C. Meanwhile, the relatively low areas near Lake Tanganyika has warmer climate with an annual temperature of 23˚C on average [8] .

Most parts of Burundi receive considerable amount of rainfall exceeding 1300 mm/year. Yet, the rainfall is generally irregular in space and time. Spatially, the north-west parts of Burundi receive heavy rains compared to other parts of the country [8] . Temporally, Burundi climate is characterized by four seasons, a short rainy season (from October to December), a short dry season (January to February), a long rainy season (March to May) and a long dry season (from June to September [30] .

Mapping malaria risk means delineating areas that are vulnerable to host breeding sites of malaria vectors and parasites. Such delineation, which was carried out as a function of a number of climatic and topographic determinants of malaria incidence, involved combining these determinants with different weights. As a result, a risk map was produced, in which various parts of Burundi were classified into three levels of risk; low, moderate and high malaria risk.

Generally, the produced malaria risk map, based on current climatic conditions, revealed major proportions of Burundi land surface have moderate-risk level, where the moderate-risk zone dispersed in all parts of Burundi covering about 78.5% of the total land surface. Meanwhile, the high-risk zone was found to be located mainly in the northern and western parts of Burundi covering about 9.1% of the total area of Burundi, which are characterized generally by a relatively low altitude, gentle slopes, high relative humidity and receive heavy rains. Such characteristics provide wet and warm conditions that are optimal for malaria vector and parasites. Meanwhile, low-risk zone, which represented about 12.4% of the total land surface, was found to be distributed in the eastern and southern parts of Burundi (Figure 4).

To assess the accuracy of the produced risk map in delineating current malaria risk as a function of current climatic conditions, the map was compared to spatial pattern of malaria morbidity at district level and the results of previous research work. For this purpose, the spatial extent of high risk zone and health

district were overlaid interest, then, the area of high-risk zone and the no of malaria cases were summarized. It was found that the high-risk zone captured 65% of the total cases of malaria that are reported at district level in Burundi in 2010 (Table 3). Moreover, high-risk zone included considerable proportions of Kyanza, Kirundi and Cibtoke provinces that were reported by Moise et al., (2016) [23] as hot spots of malaria.

Also, at province level, it was found that the number of malaria cases is positively correlated to the area of high-risk zone and in the top five provinces experiencing high rate of malaria incidence exceeding 1,000,000 cases in 2010 (Figure 5).

This generally refers to how the produced risk map reflects closely the current situation of malaria risk in Burundi under current climatic conditions, which in

turn, indicates to the validity of the developed approach in delineating malaria risk zones as a function of topographic and climatic conditions. This consequently means that the developed methodology can be employed in mapping malaria risk in the future under climate change.

As for climate conditions under climate change scenarios up to 2050, it is expected that the average annual temperature in Burundi is expected to increase by 0.9˚C and 1.3˚C on average, compared to current climatic conditions, up to 2050 under RCP 2.6 and RCP 8.5 scenarios, respectively. Generally, the eastern and southern parts of Burundi are expected to experience the highest level of temperature increase exceeding 1˚C and 1.5˚C under the two scenarios, respectively.

Similarly, most parts of Burundi are expected to receive more rainfall under climate change scenarios. The rainfall is expected to increase on average by 97 and 191 mm/year up to 2050 under RCPs 2.6 and 8.5 scenarios, respectively, compared to current rates of rainfall. Yet, the increase of rainfall will be higher in northern parts of Burundi, where the rainfall is expected to increase by 7% and 14.5% under the two scenarios, respectively. Such increasing rainfall is expected to be minimum in the southern parts of Burundi accounting for about 2 and 4% under the two scenarios, respectively. This means that the northern parts of Burundi are expected to experience wetter conditions under climate change up to 2050. In contrast, relative humidity is expected to change slightly under different scenarios ranging between 0.5% decrease and 1% increase up to 2050 under RCP 2.6 and RCP 8.5 scenarios, respectively.

Such expected changes in climate variables under different scenarios, particularly, temperature and rainfall, is thought that, will have significant impact on malaria incidence in Burundi in the future. This is especially in eastern parts that are expected to experience higher levels of warming and the northern parts that are expected to experience wetter conditions. This was emphasized by the produced malaria risk maps under RCPs 2.6 (Figure 6) and 8.5 (Figure 7) scenarios up to 2050, which revealed that high-risk zone is expected to expand generally in the northern and eastern parts of Burundi. In this respect, it is expected that, high risk zone will expand to cover about 34.6% and 44% of Burundi land surface by 2050 under RCPs 2.6 and 8.5 scenario, respectively. Meanwhile, moderate and low-risk zones are expected to cover together 65.4% and 65% of Burundi land surface by 2050 under RCP 2.6 and RCP 8.5 scenarios, respectively.

To investigate the pattern of change of different risk zones due to expected climate change, the proportions of different risk zones extent in current risk map were compared to those in risk maps under RCPs 2.6 and 8.5 scenarios by 2050. In this respect, it was found that 26% and 35% of total area of currently moderate-risk zone is expected to become high-risk zone under RCP2 2.6 and 8.5 scenarios, respectively. Meanwhile, 11% and 12% of total area of currently low-risk zone is expected to become moderate-risk zone under the same two scenarios, respectively (Table 4). This means that about 37% and 47% of Burundi are expected to experience exacerbated risk level under RCPs 2.6 and 8.5 scenarios by 2050.

It should be noted that the decreasing proportions of moderate ad low-risk-zones and increasing proportion of high-risk zone under the climate

change scenarios up to 2050 and exacerbated risk level in considerable proportions of land surface of Burundi highlight the role of climate change in increasing malaria incidence. Such considerably varied proportional area of high-risk zone under the two considered scenarios stresses on the significance of climate change in malaria incidence.

Mapping malaria risk under expected climate change in the future can contribute largely to improving resilience of Burundi to climate change associated risks and their health implications. In this respect, GIS can play a crucial role in mapping malaria risk under different scenarios of climate change and identify hot sports of malaria incidence that will need to intervention and control measure.

The developed GIS-based methodology for mapping malaria under climate change revealed that considerable parts of Burundi, particularly northern parts, will be highly vulnerable to malaria risk in the future under different scenarios of climate change.

The accuracy of mapping process of malaria risk under climate change is determined by availability of data on malaria determinants currently and in the future. Also, to improve the accuracy of mapping process, there is a need to apply an integrated analysis considering, in addition to topographic and climatic determinants, socioeconomic determinants of malaria such as income, population density, access to health services and effectiveness of malaria control measures, etc. Yet, this entails projecting these variables in the future as a prerequisite of such integrated analysis.

This research work is part of a research project sponsored by the IDRC-Canada for establishing Alexandria Research Center for Adaptation to Climate Change (ARCA) at the University of Alexandria, Egypt.

Hassaan, M.A., Abdrabo, M.A. and Masabarakiza, P. (2017) GIS-Based Model for Mapping Malaria Risk under Climate Change Case Study: Burundi. Journal of Geoscience and Environment Protection, 5, 102-117. https://doi.org/10.4236/gep.2017.511008