A review of spatial technologies with applications for malaria transmission modelling and control in Africa

Abstract

Spatial technologies, i.e. geographic information systems, remote sensing, and global positioning systems, offer an opportunity for rapid assessment of malaria endemic areas. These technologies coupled with prevalence/incidence data can provide reliable estimates of population at risk, predict disease distributions in areas that lack baseline data and provide guidance for intervention strategies, so that scarce resources can be allocated in a cost-effective manner. This review focuses on the spatial technology applications that have been used in epidemiology and control of malaria in Africa. Peer-reviewed papers identified through a PubMed search using the following keywords: geospatial technology OR Geographic Information Systems OR Remote Sensing OR Earth Observation OR Global Positioning Systems OR geospatial modelling OR malaria incidence OR malaria prevalence OR malaria risk prediction OR malaria mapping AND malaria AND Africa were used. These included mapping malaria incidence and prevalence, assessing the relationship between malaria and environmental variables as well as applications for malaria early warning systems. The potential of new spatial technology applications utilising emerging satellite information, as they hold promise to further enhance infectious risk mapping and disease prediction, are outlined. We stress current research needs to overcome some of the remaining challenges of spatial technology applications for malaria so that further and sustainable progress can be made to control and eliminate this disease. IntroductionMalaria poses serious economic, social and health burdens in tropical and subtropical countries where it is predominantly found (Gallup and Sachs, 2001). Recognising the impact of malaria on public health and economy, most African countries, with the help of interested international groups, have initiated numerous action plans as a malaria control and elimination strategy (Mendis et al., 2009). The World Health Organization (WHO) world malaria report of 2012 shows that malaria is presently endemic in 104 countries worldwide, while it is transmitted in 99 of them. According to this report, the majority of countries listed are classified as being in the malaria control phase, ten countries in the malaria pre-elimination phase and another ten countries in the malaria elimination phase. Five countries do not have ongoing transmission and are classified as being in the prevention of re-introduction phase (WHO, 2012). Although efforts of combating malaria have yielded dramatic decreases in the number of cases and deaths in most endemic regions, devastating statistics reported in the WHO (2012) report that 655,000 died from this disease, with 86% of the victims being children under 5 years of age. Furthermore, 80% of the cases reported and 91% of the total deaths of the 216 million cases worldwide occurred in Africa.It is widely acknowledged that the malaria transmission dynamics are closely related to socioeconomic, climatic and environmental factors; the latter includin...

GIS-Based Model for Mapping Malaria Risk under Climate Change Case Study: Burundi

“…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].…”

“…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.…”

Health information system strengthening and malaria elimination in Papua New Guinea

“…Spatial technologies are providing unprecedented opportunities for rapid risk assessment in malaria-endemic areas [23] by overlaying ecological data relevant to mosquito breeding and transmission, notification data and access to health services. These technologies enable the development of decision support systems for more accurate and timely responses [24], including active case detection through cluster surveys and timely case follow-up.…”

Applications of Space Technologies to Global Health: Scoping Review (Preprint)

“…On this basis, they developed and tested a model able to predict malaria evolution and thus, guide public health decisions. Applications of spatial technologies for malaria transmission modeling and control were reviewed in 2015 by Gebreslasie [48] [43,71,72,74,75,80,88], and cyanobacterias [28,81,82,123,137,138,148,150,155] were the most studied. Other studied diseases or pathogens included meningitis [225]; brucellosis [70]; C. imicola [67]; avian pathogens [25,134,136,50]; V. vulnifucus [52]; V. parahaemoliticus [52]; Fasciola hepatica [36]; hand, foot, and mouth disease [20]; Helminth infections (not limited to schistosomiasis) [120,85,21,22]; Lyme disease [108,45,79,110]; Guinea worm [30]; Nipah virus [133]; onchocerciasis [68]; opistorchiasis [146]; rotavirus [69]; typhoid fever [32]; Rift Valley fever [139,125,84]; Murray Valley encephalitis virus …”

“…GNSSs have been used often in the field of communicable diseases too, including person-to-person transmissible varieties [253,255], vector-borne diseases [21,48,38,252,258,260,257,264,256,95,263], and zoonoses [262,260,254,261,25]. In our search, the most studied communicable disease was malaria.…”