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Globally, malaria is a serious health problem with clinical cases rising annually in the tropical and subtropical countries. It is a major cause of deaths in adults and children population in the developing countries. A report published by the World Health Organization shows that close to one million cases of malaria are reported yearly, with more than 80% of these found in the Sub-Saharan Africa (WHO 2002). The efforts to control the spread of malaria continuously meet obstacles, the epidemiological situation being complex in these countries. The obstacles include an increase in the resistance to anti-malarial drugs by Plasmodium falciparum, the malaria parasite, and a rise in the insecticides resistance by the anopheles mosquito vectors responsible for transmitting the disease. The anopheles mosquito breeds in shallow areas of water suitable for mosquito and parasite development. Development of a vaccine has also been an effort by scientists to curb this disease, but this has also faced significant problems. It is evident that the efforts to control malaria will continue to rely on the control of anopheles vectors (Klinkenberg et al. 2003). Systems of computerized information management involving GIS (Geographic Information Systems) provide a powerful way of capturing, storing, and displaying the information that is spatial. It is a useful tool that assists in evidence-based decision making as well as in malaria control. Remote sensing is an art and science of obtaining information on an area, object or phenomenon by means of analyzing the data acquired through a device that is not in contact with the point of the study. Factors affecting remote sensing have potential links with malaria, including vector survival and vector habitat (Awash 2005).

With the increasing availability of remotely sensed data, researchers in epidemiology, medical entomology, and ecology associate ecological and environmental variables with the transmission of malaria. Transmission usually correlates with certain ecological and environmental parameters, and thus remote sensing can be used as a measure of these determinants. These factors can be detected by satellite imagery, which provides high spatial and temporal coverage of the earth’s surface. The combination of remote sensing and GIS techniques provide a strong tool for monitoring the environmental conditions conducive for malaria, and mapping the disease risk to human populations in the tropics. GIS can be useful in identifying risk factors, allocation of limited resources in a cost-effective means, stratification of malaria interventions, and forecasting epidemics or sudden disease outbreaks. Remote sensing can be used in studies such as vector ecology and disease transmission. In many studies of malaria control and management, remotely sensed data is useful in deriving landscape structures, vegetation cover, and water bodies.  The greatest challenge in remote sensing operations, in the tropical areas, is the persistent cloud cover, since many vector-borne diseases such as malaria are prevalent in these areas. Radar remote sensing is capable of penetrating clouds, thus providing a solution to the problem of cloud-cover usually experienced with optical satellite remote sensing. GIS routines are useful in assessing how classified land cover variables relate to the presence and abundance of malaria-carrying mosquitoes, and their proximity to populated areas, in order to generate a malaria risk map (Kazembe et al. 2006).

Importance of Mapping Malaria Risk Areas

Mapping malaria distribution and risk make it possible to focus on controlling the measures found in high-risk areas and immensely increase the cost efficiency of the programs of malaria control. Most risk maps recently developed used climatic models and information on weather data including temperature, rainfall, and humidity, as key inputs, which determine the reproduction and survival of mosquitoes, and the growth of parasites in the vector. Other studies used different indicators of vector presence, survival, and reproduction, such as land use, vegetation patterns, and soil moisture. The climatic and environmental variables correlate with epidemiological and entomological information in identifying the geographical areas posed at a high risk of malaria incidences. GIS and Remote Sensing technologies offer powerful tools in presenting spatial information on malaria risk regions. Such information provides essential implications for malaria eradication strategy to be employed. GIS allows policy makers to understand easily and view the problems and situation in relation to the available resources and target resources to the people and areas in need (Wim et al. 2003).

GIS and Remote Sensing Techniques Used to Monitor and Control Malaria

Research shows that the location and population of mosquitoes correlates with environmental factors, such as temperature, height, precipitation, humidity and vegetation. If these factors and their relation to the survival of mosquitoes are understood, and if the data for Remote Sensing containing these elements are available, GIS can help in identifying human populations at risk for disease transmission. It is clear that this approach can provide a cheaper and more effective way of targeting malaria vector control than older manual field methodologies (Alen 2002).

GIS has an ability to manipulate and integrate multiple layers or themes of spatial information for a large area, using different sources and scales. The data source of GIS includes aerial photographs, paper maps, Geographical Positioning Systems (GPS), satellite images, as well as censuses data, epidemiological surveys data, health data, environmental data, and any other data with a spatial component. GIS permits a link between maps and databases to enable data updates to be reflected automatically on the maps. It can also help to generate ranged colour maps, thematic maps, or symbol maps to imply intensity. When compared with charts and tables, maps that were developed using GIS are an effective means of clearly communication of messages even for those not familiar with the technology. GIS makes the manipulation and combination of several data themes from different sources of data easy and allows for the rapid display and analysis of multivariate spatial information. Therefore, GIS is potentially a powerful tool for epidemiology, in mapping diseases and their determinants, linking diseases and their potential risk factors, quantifying risks, and creating databases for further epidemiological and statistical analyses (Negash et al. 2005).

For malaria research and control, GIS has immense potential because it has the capacity of integrating information on all aspects of malaria including the environmental factors, demography and infrastructure. It has been extensively used in malaria research to examine the link between environmental factors and malaria transmission risk. Combining human factors and environmental data into the malaria study might indicate an existing epidemiologic situation, making it easier to control and manage the disease. Thus, Remote Sensing and GIS technologies assist epidemiologists in identifying the vector focuses. It allows them to correlate occurrence of disease indices with the environmental factors. This enables real observation of a geographic area, together with the determination of how physical factors such as rivers, vegetation, and mountains, can affect the malaria control. The use of these two techniques can also improve the acquisition of ground data and accuracy of information (Vasconcelos and Novo, 2004).

Imagery based on the satellite is characterized by temporal and spatial resolutions. For example, Worldview, Ikonos, RADARSAT-2 and SPOT-5 provide high spatial resolution images, but with low temporal resolution. The remotely sensed environmental indicators of malaria requiring spatial measurements that are precise, can be derived from high-resolution sensors, while those indicators that need temporal evaluation, for example, rainfall or vegetation, can be derived from satellites with low-spatial resolution (Beck et al, 2000). Temperature is the main malaria indicator because it influences all parts of the malaria transmission cycle. Land surface temperature (LST) estimation can be through the use of thermal infrared (IR) sensors. MODIS-Terra, GEOS, Meteosat, and AVHRR provide both night and day temperatures. LST usually correlates well with the prevailing temperature of the air, but humidity, land cover, the period of the day and atmospheric conditions can raise aberrations. On the other hand, rainfall has a spatiotemporal effect on malaria breeding sites and can be measured and evaluated directly by indirect methods. The cold cloud duration (CCD) derivation is from the Meteosat thermal IR imaging which provides estimates of rainfall based on the length of time that a cloud top falls below the threshold temperature (Vancutsem et al, 2010).

Challenges in Using GIS and Remote Sensing Techniques

The challenges facing these technologies for malaria research can be categorized into three areas. The first challenge relates to data issues. Without adequate and reliable data, GIS can not be useful. The problems include collection and reporting of accurate data on the disease, basic environmental information on land uses, vegetation, rainfall, and topography, and demographic data. The second problem relates to technology – specifically computer hardware, GIS software and training. This includes the cost of the software for its management, and the cost of site licenses for many mainstream GIS packages. The third area is a methodology, with two aspects to this issue that include availability of the appropriate software, and its usage, interpreting the results and employing the generated information in a management context. Most GIS software is not compatible with spatial statistics, which then requires an add-on module such as ESRI's Geostatistical Analyst. Little guidance in the literature is available on how to use spatial statistics. (Curran et al. 2006).

Strategies of Overcoming Obstacles

One way of overcoming problems related to the data is to set up a pilot study. This has several benefits including solving problems on a small scale before initiating a nationwide program, showing decision makers what is attainable, determining the costs involved in data collection. One problem with data collection that gives complication involves importing malaria from one place to the next through the movement of people. The main technological problem involves the acquisition of multiple copies of the GIS software. These issues can be solved in several ways. A combination of commercial software and public domain software together – like ArcView and ArcExplorer could be a solution. Data can be collected by field offices; maps should be created maps and sophisticated analyses should be performed before sending the data back to the field offices for examination. The outcome can be rapid and useful in the way of focusing resources on arising issues, with a developed way of analysis (Tanser and Le Sueur 2002). Technical considerations usually draw significant attention while little effort or thought is put into the analysis that needs to be carried out. Another common problem is mainly focusing on data collection. There is a need for a deeper analysis requiring a different expertise, as just mapping malaria incidence and prevalence is not sufficient. There need to be a strategy in using GIS beginning with data collection, acquisition of the GIS software, different types of analyses, and interpretation of these analyses (Benz et al. 2001).

There are many areas where GIS and remote sensing can be used with the purpose of understanding and controlling malaria. The most promising area is to speed up the time taken to obtain field data, which is then converted into incidence or prevalence maps. The shift to a real-time system can be of enormous help in allocating the limited resources in controlling malaria. Currently, maps provide the indicators showing where some cases of malaria exist, but usually a great amount of time is spent on using these maps in malaria control. In order to solve the limitations of these technologies, they shall be identified, discussed, and addressed. The applications of GIS should be seen in the light of the available infrastructure. As shown in this review, there are numerous ways of GIS application, from the simple mapping of malaria prevalence to the application of sophisticated risk models.

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