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Geographic Information System (GIS) has demonstrated its potential in improving the understanding of the previously incomprehensive and rather obscured epidemiological picture of malaria, by elucidating the association between the disease, its anopheline mosquito vector and the environment. The tremendous progress made in malaria transmission modelling is being facilitated by GIS. Model-based geostatistics and spatiotemporal risk models have been used to generate malaria risk maps, including; the famous global map of malaria endemicity, Global Malaria Atlas Project (MAP) and Continental MARA/ARMA Map of Africa. Numerous studies have reported the potential of GIS in malaria mapping. Adoption of these maps will guide in the planning of timely and targeted allocation of malaria intervention resources which will in turn promote cost-effectiveness and optimal outcome. However, despite these several reports of the significance of this GIS, its full potential may yet to be fully explored, particularly in high malaria burdened countries such as Nigeria. The risk of malaria was reported to be associated with water body presence, while other studies reported that perennial rainfall declined malaria risk by washing away and causing high mortality of Anopheles mosquito vectors. Georeferencing of participants residences to ascertain the actual geospatial data and entomological indices, and hence, the association between malaria risk and Anopheles mosquito abundance and distribution, for improving predictive performance of malaria risk maps were often not investigated. This may cause hindrance to the maximum exploration of the potential of GIS in accurately mapping the spatial and temporal distribution patterns of malaria.
Department of Zoology, University of Ibadan, Nigeria
Adesola Hassan
Department of Zoology, University of Ibadan, Nigeria
Kabir Popoola
Department of Zoology, University of Ibadan, Nigeria
Sunday Oladejo
Federal University of Technology, Akure, Nigeria
Dauda Awoniran
Federal University of Technology, Akure, Nigeria
*Address all correspondence to: bellobabatunde9@gmail.com
1. Introduction
The WHO African region continues to bear the highest burden of malaria, accounting for an estimated 94% of the global malaria cases and 95% of global deaths reported in 2023, with only five countries, namely Nigeria DR Congo, Uganda, Ethiopia and Mozambique accounting for 52% (Table 1). The people and communities living in poverty are the most affected, thereby further impoverishing the vulnerability conditions of the families and households. An effective approach of combating the disease should therefore involve targeting the most affected and vulnerable populations and communities, as emphasised in the 2024 WHO World Malaria Report with the theme, of addressing inequity in the global malaria response [1]. However, targeting these vulnerable population, will rely on accurate data on the environmental, biological, social, structural and economical determinants influencing the risk of malaria and the barriers hindering accessibility to services and malaria interventions. The effective harnessing of these parameters for decision-making and objective planning, implementing, and monitoring are feasible options for malaria control. However, accurate and well-organised epidemiological data on malaria determinants can only be obtained in the context of GIS-based malaria mapping studies [2].
S/N
WHO African county
Malaria cases (%)
Malaria deaths (%)
1
Nigeria
25.9
30.9
2
DR Congo
12.6
11.3
3
Uganda
4.8
2.7
4
Ethiopia
3.6
3.1
5
Mozambique
3.5
3.0
6
Tanzania
3.3
4.3
7
Angola
3.1
2.7
8
Burkina Faso
3.1
2.7
9
Mali
3.1
2.4
10
Cameroon
3.0
1.9
11
Niger
3.0
5.9
12
Cote d’Ivoire
2.8
1.8
13
Ghana
2.5
1.9
14
Madagascar
2.4
2.7
15
Benin
2.0
1.7
16
Malawi
1.8
1.2
17
Guinea
1.7
1.7
18
Chad
1.5
2.3
19
Zambia
1.4
1.4
20
Burundi
1.3
1.1
21
Kenya
1.3
1.9
22
Sudan
1.3
1.3
23
South Sudan
1.1
1.1
24
Sierra Leone
0.9
1.1
25
Togo
0.8
0.6
26
Central African Republic
0.6
0.8
27
Other African countries
1.1
1.8
TOTAL
93.5
95.3
Table 1.
Malaria cases and deaths that WHO African region accounted for in the 2023 global malaria report.
The application of survey maps and other field data and epidemiological intelligence in routine intervention programmes in many African countries dates back to the mid-1950s during the era of the Global Malaria Eradication Programme (GMEP). However, the skills and expertise required in the design of malaria intervention and control programmes on the basis of optimum comprehension of the spatial epidemiology of the disease were disused in the 1970s when malaria control agenda was put under the control of a less coordinated integrated mandate of the primary healthcare that focuses on the management of fevers. However, a renewed entreaty for optimum malaria mapping to manage malaria in Africa came up in 1996 [3], and since that time, there has been improvement on malaria-related spatial data and populations that were not available to the malaria epidemiologist several decades ago. The introduction of technological advancement that led to the emergence of Geographic Information Systems (GIS), Remote Sensing (RS) and Geographic Positioning System (GPS) in the health field and their application from simple automated epidemiological mapping to advanced satellite image analysis has further improved the understanding of malaria epidemiology in terms of the association between the disease, its vector and the environment [4]. This has even made it possible to model and map malaria risk and access to intervention in time and space via the use of Model-Based Geostatistics (MBG) [4] and spatiotemporal risk models.
The applications of GIS in health and disease epidemiology have been reviewed by several authors [5]. The tool has been used for rapid investigation of geographic distribution patterns and processes, as it has the potential to accept and process repetitive multiple operations and rapidly make comparison between several sources of spatial data and various spatial aspects, including environmental and geographic epidemiology or spatial health research. While the former entails the analysis of spatial patterns of disease and exposure to environment health risk (disease geography), the latter focuses on the spatial requirement and delivery of disease prevention intervention (health promotion), health services and health inequalities (geography of health).
Natural science approaches dominate epidemiological research, while research on health services has been under the control of social science approaches [5]. The use of overlay technique in GIS provides timely information to health experts based on early warning reports [6], such as malaria epidemic reports. The employment of GIS in the timely and targeted allocation of malaria intervention has especially become inevitable in the Sub-Saharan African countries which is home to 95% of the estimated global malaria mortality reported in 2023 [3].
2. Evolution of spatial modelling and malaria mapping
In togetherness with quality epidemiological data for evaluation, and in view of the spatial and temporal variations of malaria, there has been a substantial premise to evolve new modelling systems to fit in the prevailing heterogeneity and apply these models for evaluation. Over the past 20 years, tremendous progress has been made with respect to malaria transmission modelling based on data generated from entomological, climatic/environmental, topographic and the pattern of human settlement studies [7, 8, 9, 10].
The development of the MARA/ARMA map and the Malaria Atlas Project (MAP) are evidences of the renewed efforts towards the mapping of malaria [9]. This initiative is based on models that are generated from studies on malaria epidemiology, patterns of entomology, appropriate climatic features as well as human settlement patterns and geographic terrains. One major pitfall of the MARA/ARMA map is that the data used for mapping malaria transmission was based almost entirely on climatic variability [11, 12]. However, the MAP initiative came with some advancement over the MARA/ARMA map in that the initiative is based on the integration of survey data generated from multiple area with climatic features, thereby producing a more robust and empirical malaria risk maps and epidemiological data [9, 13]. Based on the initiative, the global map of endemicity of malaria was developed in 2007 [10]. These maps were also not without some limitations then. For instance, they were developed from a combination of several smaller-scale studies that used varying sampling techniques, time frames, and population types. In addition, there may be the appearance of selection bias, which may result when a significant proportion of the surveys selected are studies that are carried out in places where malaria endemicity is high. And lastly, huge cross-section surveys are traditionally designed with the aim of generating robust malaria estimates at the national and regional level, while district- or other microscale-level data are less common [14].
2.1 Geostatistic modelling of malaria endemicity mapping
A number of strategies have been used to develop continuous maps of malaria endemicity using data from malariometric studies. However, these data will require the application of a geostatistical model for the prediction of malaria endemicity values at geographic locations where there is unavailability of survey data [15]. The malaria maps generated from such models are prone to intrinsic unreliability and its evaluation remains a major issue in mapping disease.
Past surveys employed a predictive Bayesian Geostatistic framework [4] for spatially predicting the endemicity of malaria [16], due to the model’s capacity to generate a valid statistical explanation of the classical geostatistical tool for spatial malariometric data analysis while also giving room for the accommodating the MBG methods of inferential statistics [17, 18]. The generated maps can thus provide valid contemporary global maps of malaria endemicity, when it is based on the assessment of confidence in the prediction using MBG and provide a clear description of the reliability of the prediction for the users of such maps [4]. One main underlying principle of the geostatic model is that the uncertainty of mapped predictions increases with decreasing quantity of and increasing distance away from close data points. This principle can be applied through different times and spaces, if the data are obtained at varying times and locations.
Bayesian geostatistics approach of generating malaria risk maps using climatic variables has promoted the adoption of these survey data in advanced geostatistics and epidemiological modelling techniques [18]. Unlike the traditional classical statistical methods, Bayesian modelling allows for the embracing and progressively updating of prior knowledge relating to the parameters of the models [19], thereby promoting greater pliability in modelling parameters that are not known and evaluating the probability distribution in a specified estimate. With this advantage, Bayesian modelling has proven to be highly relevant in malaria prevalence mapping when there are vast areas to sample or when there are certain unsampled periods and when some observed covariates are not precise or well defined [20]. By applying spatial and temporal autocorrelation, data can be extrapolated from neighbouring places or from sampled time frames to generate estimates from unsampled places and time frames, respectively [20].
The Bayesian geostatistics techniques use statistical inference from Bayesian analysis to accommodate distinct environmental variables, spatially referenced point data sets, and corresponding spatial autocorrelation to be concurrently modelled and predicted on an uninterrupted smooth surface and for the determination of uncertainty [21]. There is available evidence that this method has successfully predicted the spatial distribution of malaria transmission, thereby guiding in targeting intervention efforts [14, 22]. For instance, spatial distribution patterns of malaria were evaluated, and a Bayesian predictive model was generated in Zambia based on the data from the Malaria Indicator Survey (MIS) for the year 2006 [22]. With the current renewed effort to eliminate malaria, mapping malaria foci has proposed a new rationale for targeting surveillance and control of malaria [23]. However, a major challenge then was that no geostatistical models were applied for malaria control programme evaluation with survey data that are routinely obtained. Thus, there is a need for improved integration of these approaches into vast programme evaluation impact as this will facilitate adequate control for confounding environmental and climatic factors and spatial autocorrelation.
Previously developed routine cross-sectional surveys at the national scale have been used to generate spatial risk patterns of malaria in several settings; however, routine cross-sectional malaria surveys are yet to be applied in the evaluation of the spatial patterns of distribution of malaria at different time frames with regard to the non-static level of intervention coverage and changing weather patterns. The application of these methods in this form of evaluation process generates vital information that enhances the understanding of the relative influence of the climatic factors and intervention intensification (or decline) with respect to geographically specific varying prevalence rates of malaria. Additionally, the specific parasite survey timings may substantially result in overreporting of the outcome of the prevalence of malaria, which is dependent on rainfall peak periods. Conduction of malaria prevalence survey few months sequel to the peak of the rains, may confound the comparison of the outcome with other years [24]. A large proportion of national cross-sectional data used for spatial analysis studies are based on only one survey to map malaria risk or on myriads of surveys aggregated into a single risk map. Consequently, such risk maps may not depict the real patterns of distribution in years owing to the changing annual climatic parameters. Furthermore, there was a dearth of Bayesian mapping data that incorporates temporal aspects of malaria [24], particularly for malaria prevalence surveys.
2.2 Role of malaria cartography in planning malaria interventions
The correct knowledge regarding the patterns of malaria distribution is highly essential in the planning and evaluation of malaria control interventions [3]. Malaria mapping is highly relevant in all aspects of malaria control efforts and coordination [9]. For instance, the summit meeting of the Sub-Saharan African region held in Nigeria in 2000 with respect to the malaria situation in the country reported a complete lack of detailed and comparable malaria data and, hence, advocated, among other things, for the need to carry out more researches regarding malaria incidence and prevalence, clinical malaria epidemiology and the disease epidemics [25].
In an environment made up of international policymakers, in which the control of malaria has been forced to re-strategise malaria elimination plausibility, malaria cartography can be considered as a highly important and imperative strategy to plan, implement and evaluate the impact of intervention efforts [26]. One integral part of the Spatial Decision Support System (SDSS) is malaria prevalence and incidence mapping. For instance, following the review of the existing SDSS for malaria elimination by Kelly et al. [27], improved planning, implementation of the intervention programmes and monitoring and evaluation of the intervention programmes was suggested. The implementation of trial SDSS applications has been implemented in a number of malaria interventions in Southern Africa [28]. In the same vein, Diallo et al. [29] used an innovative subnational tailoring approach to identify the most appropriate interventions in Guinea, considering the resources that were available. The use of local data to inform eligibility and prioritisation fostered the identification of the optimal mix of interventions.
2.2.1 Global and continental malaria cartography: Pros and cons
Malaria model risk maps at the global and continental scales have previously been developed in accordance with expert opinions and option rules [30] and later on climate favourability [11]. The foremost P. falciparum malaria endemicity digitised risk map at the global scale was developed by Lysenko and Semashko [31]. This represented the best traditions of manual cartography that placed emphasis on the acceptance of a wide range of various sources of data and integrating them into a single synthesised map without any form of formal underlying quantitative system [9]. The map provided a comprehensive description of the classic state of malaria endemicity, each of which relates to the prevalence of malaria in children.
The map provided impressive information regarding the state of knowledge of global malaria endemicity during that period; however, the speculative nature of the approach was not without some setbacks, the most important of which is the less likely accuracy of the map and its variability from place to place, which is not measurable and hence, cannot be communicated to end-users. These important deficiencies have placed a basic limitation on the application of this map for making decisions regarding critical public health interventions [32].
Over the five decades that the Lysenko map came into existence, there was very little effort to make improvements on the map during that period. However, the development of the continental-scale MARA/ARMA map in the year 1997 and the global MAP developed in 2005, led to a rebirth which facilitated the successful transformation of the science of malaria mapping studies and its application in the control, elimination and ultimate eradication of malaria [32].
2.2.2 Contemporary maps of malaria endemicity
Malariometric data-based empirical maps have hitherto been published, which ranged from large spatial scales, such as national- and regional [3, 12, 33], to small spatial scales, such as village and settlement [34, 35]. National- and regional-scale malariometric maps are advantageous in that it has approximate homogeneity of variables relating to malaria intervention and control. However, these maps tend to disregard the broader view of the effects that are not within the political borders of the country under investigation [32], as geographic variability also occurs, even within smaller-scale entities such as within settlements [35].
The previous global distribution maps of malaria witnessed a number of setbacks [36, 37], the most important of which are the use of a partial description of the data, basing the definition of the risk contours on irrational and insufficiently explained expert-opinion rules and estimations of the uncertainty surrounding the predictions were not given. However, the development of contemporary maps of malaria endemicity at the county/regional level has overcome the deficiencies with the global scale malaria maps
2.3 GIS technology in malaria prevalence mapping
The main significance of adopting GIS technology in the epidemiological mapping of malaria is that such maps give an additional spatial context in relation to the analysis of data, which aids in the visualisation of the complex patterns of the disease. For instance, accurate malaria mapping was identified as a vital tool that can be used to promote malaria vector intervention efforts [4]. There are arrays of studies that have utilised spatial technologies in mapping malaria prevalence (Table 2).
Country of study
Research objective
Research method
Key findings
References
Somalia
Mapping malaria distribution in low transmission regions
Spatial clustering analysis
Malaria transmission varied from hypo- to meso-endemic
GIS-based malaria incidence and prevalence mapping studies in Africa.
However, the majority of these maps were developed at unrefined spatial resolutions, thereby limiting their operational application in malaria control programmes at lower administrative spatial scales. Small spatial scale maps, some of which were based on specific georeferenced point malaria prevalence data, have also been developed by various authors [16, 29, 34, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48]. For example, GIS was used to map malaria prevalence in Botswana based on reported cases of malaria at the local health centres [39].
A regional map of malaria showing the spatial patterns of distribution of P. falciparum malaria (Figure 1) was also developed in Ethiopia [49]. In the same vein, malaria prevalence map across Somalia, which was based on malaria data that were obtained from previous routine cluster and malaria cluster surveys, was also developed [16]. According to the map, the transmission pattern of malaria ranged from hypo-endemic to meso-endemic in Somalia. The map identified locations in proximity to the Shabella River and Juba as the primary malaria transmission foci in Somalia. The incidence of malaria was developed at a smaller scale in a village in Central Ethiopia, where malaria transmission is unstable [34].
Figure 1.
Spatial heterogeneity of Malaria (P. falciparum) in Ethiopia. Source: Federal Democratic Republic of Ethiopia Ministry of Health [49].
Structured GPS survey questionnaires were developed by these authors to record the geographic location of participants along with relevant demography. A spatial clustering method was adopted in mapping malaria incidence in addition to estimating the malaria incidence density of 1000 people per year for each of the villages that were georeferenced. Malaria prevalence mapping has fostered a growing interest in the modelling of the endemicity of malaria in relation to environmental risk variables. The adoption of GPS-based malaria prevalence and entomological parameters in malaria mapping will aid in the generation of the true epidemiological picture of the association between malaria and related environmental indices, and hence, the development of an accurate small spatial scale malaria map. This was especially demonstrated in a study by Bello and Hassan [50]. The authors used malaria-related data collected from participants whose households were georeferenced to generate risk maps of malaria showing the spatial distribution patterns of the disease and also identified areas where malaria clustering occurred. In the same vein, Kibibi [51] explored the use of GIS for mapping malaria transmission hotspots in West and East Africa. The authors found that recent GIS underscore the association between malaria transmission and environmental and socioeconomic factors.
For instance, epidemiological surveys that extends spatial geostatistical model to accommodate time are not common, however, a full spatial and temporal geostatistical modelling techniques has evolved [24, 49]. By incorporating the dimension of time, unambiguous comparison between future malaria map iterations and the benchmark is guaranteed. The map will hence produce an explicit description of the geographic framework, which can be used to survey, monitor and evaluate the effectiveness of global malaria intervention efforts.
Nigeria is not left out in the studies on malaria epidemiology using GIS spatial technology tools. However, there are few such studies at the country level [52, 53] while the other researches are confined to a few states [54, 55, 56, 57, 58, 59]. Malariometric data spanning a period of 15 years, from 1993 to 2007, for 23 states in Nigeria, were obtained from the Data Bank of WHO and the Epidemiological Unit/Roll Back Malaria of both the Federal and State Health Ministries. The sum of monthly malaria cases for the 15 years period were aggregated by months, which represented the study variables. Principal Component Analysis (PCA) analysis was then used to analyse the spatial distribution patterns of malaria, after which the map showing the spatial distribution patterns of malaria in Nigeria was developed [52]. The main components were recognised on the basis of seasonality, namely, component 1 (dry season), component 2 (rainy season) and component 3 (transition from rainy to dry season).
For the purpose of deriving comprehensive observations and accurate spatial distribution patterns of malaria in Nigeria, the authors grouped Nigeria states into three categories on the basis of the level of malaria transmission intensity, namely: high-level (>1.00), medium-level (1.00–0.01) and low-level (<0.01) malaria transmission zones (Figure 2). According to the map, Lagos state was located within the low-level malaria transmission zone; however, following the dry season (October to March), Lagos migrated from the low-level to the high-level malaria transmission zone (1.222), which highlights the significance of seasonal variation in malaria transmission [52].
Figure 2.
Spatial variation in malaria infestation in Nigeria. Source: Onwuemele [54].
According to Akpan [53], based on environmental suitability, An. gambiae s.s., An. gambiae s.l. and An. arabiensis is most widespread in the Derived savannah and humid forest, moderately distributed in the Northern and Southern Guinea savannahs, and limited distribution in the Mid-Altitude zones and Sahel savannah. An. arabiensis is widespread throughout all the Nigerian states, with the highest recorded in a Southwestern geopolitical region state (Lagos) and lowest in South-South geopolitical region state (Bayelsa). The mean abundance of An. gambiae s.l. was also highest in Lagos state, while the least was recorded in Yobe state. Omogunloye et al. [59] used GIS technology to analyse the influence of environmental risk parameters, namely temperature, rainfall, and relative humidity, on malaria cases in Lagos state.
A risk map of malaria, showing all the 20 local government areas in the state and the corresponding level of malaria cases (high and low), was generated. In addition, a significant association was observed between malaria cases and the corresponding environmental features; hence, a spatial clustering of malaria and a predictive model was developed.
2.4 Application of GIS in the assessment of environmental risk variables
The dynamics of transmission of endemic malaria have since been related to the abundance and distribution patterns of the Anopheles mosquito vectors, which in turn is related to the availability of favourable and preferred breeding habitats as well as to favourable environmental features. A lot of studies have reported associations between the abundance and patterns of distribution of Anopheles mosquitoes and climatic variables, mostly concentrated on country or regional scales [47]. The primary objectives of these studies were to investigate the statistical associations between malaria and environmental risk variables. For example, Gething et al. [60] used the technique in their study and found an estimated 2.6 billion who live in the tropics to be at risk of P. falciparum malaria, while a relatively smaller number of humans (2.5 billion) were found to be at risk of P. vivax malaria. In the same vein, map showing the spatial heterogeneity of the endemicity of P. falciparum malaria has also been developed [10].
The significance of environment on malaria distribution was demonstrated in Epe and Orimedu in a study by Bello and Hassan [50]. The characteristic distinct terrain explained the significant spatial heterogeneity and clustering of malaria in Epe while the very high malaria risk terrain in Orimedu was attributed to the fairly evenly distributed high malaria risk terrain. Topographic terrain and perennial water body presence have also been incriminated in determining the patterns of the distribution of malaria.
Steep topographic terrain was characterised by distinct altitude zones. Service [61] reported that inhabitants in upper altitude zones where there is absence of perennial water bodies such as river were forced to remain outside in the late evening to get fresh breeze before going to bed. This was substantiated in another study where malaria prevalence was significantly lower in the lower altitude zone in Epe, where the river present in the area provides a relatively cooler environment and fresher air. Consequently, eliminating or at least limiting the need to stay outside in the late evening to get fresh breeze [50]. In a similar study, villages that were classified as hotspots of malaria transmission were attributed to environmental parameters characterised by abundant water bodies [62].
The allocation of timely and targeted malaria intervention has fostered cost-effectiveness, hence, increasing the likelihood of the successful elimination of the disease. This approach is informed by accurate data on the epidemiological picture and distribution patterns of the diseases, in which GIS has proven to be a potential tool in its acquisition. However, the available small-scale predictive risk maps of malaria still suffer from inaccuracies due to lack of georeferenced data on malaria source. This has limited the full exploration of the potential of GIS in providing accurate maps of the spatial and temporal distribution patterns of malaria.
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