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Global positioning system--a tool to strengthen malaria research and control.

During the last decade, there has been a surge in publications related to Geographical Information System (GIS) and Remote Sensing (RS) in malaria research and control. The variety of analysis presented using GIS-RS platform demonstrates tremendous capabilities of the technology available to epidemiologists and researchers during recent times (1-2). The data used for GISRS works is mostly provided from satellite imageries, aerial photographs, topo-sheets, published/digitized maps and census surveys; however, data specific to an application may not be readily available and have to be generated from fields. Global Positioning System (GPS) is the new data acquisition tool which has tremendous applications in data collection, precision surveying and mapping thus enabling transfer of field generated data into office based GIS-RS system for spatial analysis.

The Global Positioning System--a satellite-based navigation system was developed by U. S. Department of Defense to provide a consistent and accurate method of determining locations. The GPS is made up of a network of 24 satellites that are continuously orbiting the earth, 24 h a day, in all weather conditions. There is no subscription or fee to use GPS services except buying receiver equipment which receives signals from minimum four GPS satellites and uses the transmitted information to calculate user's position in terms of latitude, longitude and altitude. Basic GPS consists of a single receiver unit while differential GPS (DGPS) consists of two devices-one stationary at a known location and another roving in the field which makes measurements. DGPS gives better accuracy in comparison to basic GPS operations.

GPS can be termed a technology that compliments GIS-RS operations. The GPS, GIS-RS integration can be achieved by transferring data from a GPS receiver to existing GIS-RS application to build either a new database or to update existing information. In a GPS, collected location data is saved as a waypoint which is a set of coordinates that identifies a point on the surface of the earth and navigated lines connecting waypoints describe track route of the path traveled during surveys (3). The GPS data can be used to geo-reference already existing maps/imageries (4), site selection to plan field surveys (5-6) and to display specific features present in a study site (7). GPS can be particularly useful in generating maps of small study areas where accurate maps are not readily available (8-9). Challenges in using GPS and GIS-RS technology have been overcome with the availability of inexpensive and easy to use GPS receiver equipment, GIS software, improved remote sensing sensors and cut down in imagery cost.

This write-up attempts to present GPS technology in conjunction with GIS-RS applications in malaria research and control. In particular, the applications of GPS in the context of (i) Mapping larval breeding habitats of anopheline mosquito; (ii) Mapping spatial distribution/risk of malaria; (iii) Mapping malaria related treatment seeking/availability patterns; and (iv) Integrated GPS and personal digital assistant (PDA) device for household insecticide-treated nets (ITNs) surveys have been discussed along with future trends.

GPS with GIS-RS in Malaria Research and Control Mapping larval breeding habitats of anopheline mosquito

The larval ecology of malaria vectors has long been a neglected area of vector research (10). One major reason being the challenges involved in identification of aquatic habitats in the field. With the advent of new tools like GISRS and GPS, the use of larval habitat maps has become increasingly popular in various applications related to management of malaria.

Accurate mapping of spatial distribution of vector breeding habitats is useful to define malaria epidemiology/distribution pattern. A local survey of the larval breeding habitats of malaria vector An. arabiensis on the highlands and lowlands around Mount Kenya carried out using handheld GPS receiver, mapped using a GIS and combined with malaria data indicated indigenous malaria transmission in highland areas (11). GPS based mapping of anopheline breeding sites combined with local epidemiological data indicated a gradient of endemicity between the urban centre and the periphery of Ouagadougou city in Burkina Faso (12). GPS recordings of anopheline-positive larval habitats taken during both dry and rainy seasons were analyzed which identified temporal variations in spatial distribution of habitats thus explaining the variation in epidemiology of malaria in a small study area located in western Kenya (13). Anopheline density varied as a function of distance from dwellings to the potential breeding sites, both located by GPS thus explaining the variability in malaria transmission in a small village in the mainland region of Equatorial Guinea (14).

Satellite imagery-based prediction models are useful to identify the larval breeding habitats where GPS generated ground data can be utilized to evaluate them. DGPS played key role in a study design assessing the utility of RS imageries obtained from different sources viz. Landsat TM 7, Ikonos satellites and aerial photographs combined with terrain modeling to correctly predict larval habitats of malaria vectors in a village of western Kenya highlands. One-meter spatial resolution Ikonos images identified most of the anopheline larval habitats followed by aerial photos and Landsat TM 7 images (3). Terrain-based modeling approaches based on satellite imageries such as Shuttle Radar Topography Mission (SRTM) and Advanced Spacebome Thermal Emission and Reflection Radiometer (ASTER) for predicting abundance and the distribution of anopheline mosquito larval habitats were evaluated using GPS based ground surveying of habitats in southern Zambia (15).

Studies were conducted using GPS based larval habitat mapping to understand the role of vector species involved in malaria transmission. Overlaying of GPS recorded breeding habitats during high and low water period of Lake Victoria on Quick Bird imagery in a GIS determined increasing number of habitats of An. funestus on newly emerged land, thereby identifying increasing role of the species in malaria transmission in lake basin (16). Land use changes were recorded in a village of western Kenya over a period of four year by adding polygons generated with a handheld GPS to a pre-existing GIS map of land use and land cover (LULC). Locations of all the aquatic habitats were recorded by GPS and overlaid on LULC map. Changes in LULC were found favorable to the development of An. gambiae larval habitats which increased malaria transmission risk (6). GPS and GIS were used to prepare map of larval breeding habitats to understand the role of An. gambiae in disease transmission in a village of western Kenya (17). Integrated maps of the mosquito breeding sites recorded using GPS during six surveys were key input to the model used for predicting the distribution and abundance of adult mosquito vector and identified the role of An. gamble in malaria transmission in a village located in the highlands of a most densely populated district of western Kenya (18). Survey locations where larval breeding habitats of malaria vector An. minimus were found, were recorded using handheld GPS in selected primary health centers (PHCs) of Sonitpur and Nagaon districts of Assam, India. GPS locations were mapped in a GIS (Fig), collected fever data were linked and further analysis identified the role of the vector in disease transmission (19).


Spatial distribution pattern of larval habitats in different seasons was explained by point pattern analysis of GPS recorded locations of anopheline positive larval habitats in a study carried out in western Kenya highlands. It was found that the degree of aggregation of larval habitats in valley bottoms was higher in the dry season than in the rainy season. Larval interventions during dry season were suggested for effective control (20).

Spatial distribution of GPS collected larval breeding sites created as a layer and overlaid on the land use-land cover identified most commonly associated land covers with anopheline habitats in studies carried out in different countries. The larvae of An. gambiae were more frequently located in farmlands and pastures than in the forests in a study carried out in western Kenya highlands (20). Most of the larval habitats of An. gambiae/An. funestus were located within the mature maize land cover followed by grasslands and newly cultivated fields in another study carried out in western Kenya lowlands (21). Flooded rice paddies were the most commonly associated habitats of An. sinensis on an island and a neighboring district of the Republic of Korea (22).

Identification of spatial and temporal distribution of larval breeding habitats using geospatial tools (GIS, RS, GPS) was considered practical for cost-effective planning to reduce malaria risk in northern Sudan than targeting the sparsely distributed adult vector mosquito An. arabiensis population (23).

The emergence of insecticide resistance in Anopheles mosquitoes has become a serious concern to the success of malaria control. A resistance monitoring survey in An. gambiae in forty districts of southern Benin used GPS collected geographical information of the localities where mosquito larvae and pupae were collected. The collected specimens were reared up to adult emergence. GPS locations were mapped in a GIS, and insecticide resistance data were linked. The generated map showed a widespread resistance to permethrin in An. gambiae populations in most districts of south Benin with the exception of three districts where no resistance was recorded. Bendiocarb was suggested as an alternative insecticide to pyrethroids for indoor residual spraying (IRS) in Benin (24).

Mapping spatial distribution/risk of malaria

Understanding spatial distribution/risk of malaria is important for planning effective control, priority treating of identified high-risk zones and to help distribution of limited resources more efficiently. These studies have been carried out at various spatial scales with household being the lowest unit.

GPS data combined in a GIS-RS were used in studies to map distribution of disease over time and space and to detect clusters at high risk of malaria. Households geo-referenced by GPS and combined with generated epidemiological data helped analyze spatial distribution of malaria in south-eastern regions of Bangladesh. Cluster analysis using GPS data identified malaria hot-spots in the study area for consideration regarding planning effective control measures (25). GPS based geo-referencing of households combined with malaria incidence data integrated into a GIS system along with cluster analysis helped determine high-risk clusters of malaria cases in Mali and Tanzania (26-27). DGPS referenced household information combined with GIS derived topographic wetness index was used to predict households at greatest malaria risk in two communities of western Kenyan highlands (28). Community wise parasitological surveys geo-referenced using GPS and mapped using GIS combined with geo-statistical analysis helped spatial prediction of Plasmodium falciparum prevalence in Somalia (29). The analysis of the spatial and temporal distribution of the malaria infections in Ninh Thuan province of Vietnam used geo-referenced locations of all the 43 study villages, overlaid on land-cover digital map using GIS with parasite rate and seroprevalence data reported for each village (30). In Kenya, a cluster analysis of series of large-scale school malaria survey recorded by GPS and combined with malaria epidemiological data identified eleven schools located around lake Victoria having high parasite prevalence (31).

GPS has essentially been the part of the study designs monitoring malaria risk and providing spatial guidelines regarding implementation of malaria control programmes. GPS locations of the health facilities and the study villages were recorded for GIS-mapping in a province in Laos. Attribute data related to the use of insecticides treated nets (ITNs) were collected through structured questionnaire and health outcome through malaria rapid diagnostic tests (RDTs) at household level and pooled up village-wise and attached to the developed GIS map in order to monitor malaria risk in the study villages. Lower intervention coverage was identified in distal villages where several malaria cases were detected thereby providing essential information to control staff in targeting limited financial and human resources for controlling malaria within the province (32). Developing a GPS referenced active case detection system to identify the parasite refugia within asymptomatic reservoirs was found critical to monitoring of malaria risk and development of malaria elimination programmes in the southern province of Zambia (33).

Mapping malaria related treatment seeking/availability patterns

Studies explaining treatment seeking/availability patterns of malaria used GPS to geo-reference households, health facilities and drug outlets and calculate distances in a GIS to provide significant results. Closeness to health facilities run by the malaria control programme and drug outlets was significantly associated with the choice of treatment in Rajasthali subdistrict of Rangamati district in Bangladesh (34). A study carried out in Tanzania identified that the patients living in villages with either a drug shop or a health facility in the vicinity were four times likely to get prompt and effective malaria treatment compared to people from villages without these facilities (35). Despite long distances of the temporary paddy field houses from health services, 56% of the malaria fever episodes were treated at health facilities, indicating home-management of cases being less common in study sites located in Kilombero Valley of Tanzania (36). In three study districts of Somalia, it was found that the availability of public health services was relatively low with only 45 public health facilities for approximately 0.6 million people with median distance to health facilities being 6 km (37).

Integrated GPS and personal digital assistant (PDA) device for household ITN survey

Conducting household surveys in areas where detailed maps are not available is a challenging task. Also conducting paper based surveys, following elaborate skip patterns and data accuracy checks can be difficult to achieve (38). The application of integrated GPS and PDA devices using GPS survey selection software, developed by the Centers for Disease Control and Prevention (CDC), USA is useful in assuring data quality, obtaining a statistically accurate random sample within geospatial data and rapidly making data available for analysis (39). By combining PDAs and GPS for surveys, field teams can quickly generate a complete list of households at the enumeration area by mapping the location of each household, select a computer-generated simple random sample and return to the selected households using the GPS navigation tool to conduct a PDA-based interview.

This technology helped researchers to collect high-quality, statistically valid and population-representative data on insecticide-treated bed net (ITN) ownership and usage from several thousand households across five nations of Togo, Niger, Kenya, Madagascar, and Sierra Leone (40). Also the impact of distribution of free ITNs on childhood malaria morbidity was evaluated in Togo and Mozambique nations using this integrated technology (41,42).

Use of the technology helped rapid generation and sharing of survey data which helped data-driven decision making and influencing the control programmes. A total of 21,588 houses in 162 enumeration areas in Togo nation of Africa were mapped in 19 days using 24 field workers to evaluate household ITN ownership and usage by pregnant women and children less than five years of age. Using GPS software application, mapping of houses was completed in mean mapping time of 1 hour and 48 minutes. Preliminary results of the coverage survey were presented to the Ministry of Health within two days of completion of data collection (43).

The Future

The aforementioned applications of GPS in conjunction with GIS-RS indicate that this tool has been used worldwide in numerous ways to strengthen malaria research and control programmes. Wide Area Augmentation System (WAAS) enabled GPS are providing better positional accuracy than DGPS (44). Currently, WAAS satellite coverage is available only in USA and in India WAAS-equivalent system namely GAGAN (GPS Aided Geo Augmented Navigation) is being implemented by the government and once ready, high-precision GPS data will be available (45). With this advancement, in future an extended role of GPS in malaria epidemiological studies is envisaged.


(1.) Tanser, F.C. and Sueur, D. The application of geographical information systems to important public health problems in Africa. Int J Health Geograph 1:4, 2002.

(2.) Saxena, R., Nagpal, B.N., Srivastava, A., Gupta S.K. and Dash, A.P. Application of spatial technology in malaria research and control: some new insights. Indian J Med Res 130:125, 2009.

(3.) Srivastava, A. and Nagpal, B.N. Precision mosquito survey using GIS : A case study of An. minimus--A foothill vector of malaria in India. ICMR Bull 40:41, 2010.

(4.) Mushinzimana, E., Munga, S., Minakawa, N., Li, L., Feng, C, Bian, L., Kitron, U., Schmidt, C, Beck, L., Zhou, G., Githeko, A.K. and Yan, G. Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malaria J 5;13 doi: 10.1186/1475-2875-5-13, 2006.

(5.) Vittor, A.Y., Gilman, R.H., Tielsch, J., Glass, G., Shields, T, Lozano, W.S., Pinedo-Cancino, V. and Patz, J.A. The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of falciparum malaria in the Peruvian Amazon. Am J Trop Med Hyg 74:3, 2006.

(6.) Bogh, C, Lindsay, S.W., Clarke, S.E., Dean, A, Jawara, M., Pinder, M. and Thomas, C.J. High spatial resolution mapping of malaria transmission risk in the Gambia, west Africa, using Landsat TM satellite imagery. Am J Trop Med Hyg 76:875, 2007.

(7.) Munga, S., Yakob, L., Mushinzimana, E., Zhou, G., Ouna, T, Minakawa, N., Githeko, A., and Yan, G. Land use and land cover changes and spatiotemporal dynamics of anopheline larval habitats during a four-year period in a highland community of Africa. Am J Trop Med Hyg 81: 1079, 2009.

(8.) Jacob, B.G., Muturi, E.J., Funes, J.E., Shililu, J.I., Githure, J.I., Kakoma, 1.1., and Novak, R.J.A grid-based infrastructure for ecological forecasting of rice land Anopheles arabiensis aquatic larval habitats. Malaria J 5:91 doi: 10.1186/1475-2875-5-91, 2006.

(9.) Jacob, B.G., Muturi, E., Halbig P., Mwangangi, J., Wanjogu, R.K., Mpanga, E., Funes, J., Shililu J., Githure, J., Regens, J.L. and Novak, R.J. Environmental abundance of anopheles (Diptera:Culicidae) larval habitats on land cover change sites in Karima village, Mwea rice scheme, Kenya. Am J Trop Med Hyg, 76:73, 2007.

(10.) Minakawa, N., Mutero, CM., Githure J.I., Beier, J.C. and Yan, G. Spatial distribution and habitat characterization of anopheline mosquito larvae in western Kenya. Am J Trop Med Hyg 61:1010, 1999.

(11.) Chen, H., Githeko, A.K., Zhou, G., Githure, J.I. and Yan, G. New records of Anopheles arabiensis breeding on the Mount Kenya highlands indicate indigenous malaria transmission. Malaria J 5:17 doi:10.1186/1475-2875-517, 2006.

(12.) Wang, S., Lengeler, C, Smith, T.A., Vounatsou, P., Diadie D. A., Pritroipa, X., Convelbo, N., Kientga, M. and Tanner, M. Rapid urban malaria appraisal (RUMA) I: Epidemiology of urban malaria in Ouagadougou. Malaria J 4:43 doi: 10.1186/1475-2875-4-43, 2005.

(13.) Li, L., Bian, L., Yakob, L., Zhou, G. and Yan, G. Temporal and spatial stability of Anopheles gambiae larval habitat distribution in Western Kenya highlands Int J Health Geograph 8:70 doi:10.1186/1476-072X-8-70, 2009.

(14.) Cano, J., Descalzo, M.A., Moreno, M., Chen, Z., Nzambo, S., Bobuakasi, L., Buatiche, J.N., Ondo, M., Micha, F. and Benito, A. Spatial variability in the density, distribution and vectorial capacity of anopheline species in a high transmission village (Equatorial Guinea). Malaria J 5:21 doi: 10.1186/1475-2875-4-43, 2006.

(15.) Clennon, J.A., Kamanga, A., Musapa, M., Sniff, C. and Glass, G.E. Identifying malaria vector breeding habitats with remote sensing data and terrain-based landscape indices in Zambia. Int J Health Geograph 9:58 doi: 10.1186/1476-072X-9-58, 2010.

(16.) Minakawa, N., Sonye, G., Dida, G., Futami, K. and Kaneko, S. Recent reduction in the water level of Lake Victoria has created more habitats for Anopheles funestus. Malaria J 7:119 doi:10.1186/1475-2875-7-119, 2008.

(17.) Mutuku, F.M., Alaii, J.A., Bayoh, M.N., Gimnig, J.E., Vulule, J.M., Walker, E.D., Kabiru, E. and Hawley, W.A. Distribution, description and local knowledge of larval habitats of Anopheles gambiae s.l. in a village in western Kenya. Am J Trop Med Hyg, 74:44, 2006.

(18.) Li, L., Bian, L. and Yan, G. A study of the distribution and abundance of the adult malaria vector in western Kenya highlands. Int J Health Geograph 7:50 doi:10.1186/1476072X-7-50, 2008.

(19.) Annual Report. National Institute of Malaria Research, New Delhi, p. 38, 2008-09.

(20.) Minakawa, N., Munga, S., Atieli, F, Mushinzimana, E., Zhou, G., Githeko, A.K. and Yan, G. Spatial distribution of anopheline larval habitats in Western Kenya highlands: Effects of land cover types and topography. Am J Trop Med Hyg 73:157, 2005.

(21.) Mutukul, F.M., Bayoh, M.N., Hightower, A.W., Vulule, J.M., Gimnig, J.E., Mueke, J.M., Amimo, FA. and Walker, E. D. A supervised land cover classification of a western Kenya lowland endemic for human malaria: associations of land cover with larval anopheles habitats. Int J Health Geograph 8:19doi:10.1186/1476-072X-8-19, 2009.

(22.) Sithiprasasna, R., Lee, W.J., Ugsang, D.M. and Linthicum, K.J. Identification and characterization of larval and adult anopheline mosquito habitats in the Republic of Korea: potential use of remotely sensed data to estimate mosquito distributions. Int J Health Geograph 4:17 doi: 10.1186/1476-072X-4-17, 2005.

(23.) Ageep, T.B., Cox, J., Hassan, M.M., Knols, B.G., Benedict, M.Q., Malcolm, C.A., Babiker, A. and Sayed, B.B.E. Spatial and temporal distribution of the malaria mosquito Anopheles arabiensis in northern Sudan: influence of environmental factors and implications for vector control. Malaria J 8:123 doi: 10.1186/1475-2875-8-123, 2009.

(24.) Yadouletonl, A.W., Padonou, G., Asidi, A., Moiroux, N., Bio-Banganna, S., Corbel, V., N'guessan, R., Gbenou, D., Yacoubou, I, Gazard, K. and Akogbeto, M.C. Insecticide resistance status in Anopheles gambiae in southern Benin.Malaria J9:83doi:10.1186/1475-2875-9-83, 2010.

(25.) Haque, U., Huda, M., Hossain, A, Ahmed, S.M., Moniruzzaman, M. and Haque, R. Spatial malaria epidemiology in Bangladeshi highlands. Malaria J 8:185 doi: 10.1186/1475-2875-8-185, 2009.

(26.) Gaudart, J., Poudiougou, B., Dicko, A., Ranque, S., Toure, O., Sagara, I., Diallo, M., Diawara, S., Ouattara, A., Diakite, M. and Doumbo, O.K.. Space-time clustering of childhood malaria at the household level: a dynamic cohort in a Mali village. BMC Public Health 6:286 doi: 10.1186/1471-2458-6-286, 2006.

(27.) Winskill, P., Rowland, M., Mtove, G., Malima, R.C. and Kirby, M.J. Malaria risk factors in north-east Tanzania. Malaria J 70:98 doi:10.1186/1475-2875-10-98, 2011.

(28.) Cohen, J.M., Ernst, K.C., Lindblade, K.A., Vulule, J.M., John, C.C. and Wilson, M.L. Topography-derived wetness indices are associated with household-level malaria risk in two communities in the western Kenyan highlands. Malaria J 7:40 doi: 10.1186/1475-2875-7-40, 2008.

(29.) Noor, A.M., Clements, AC, Gething, P.W., Moloney, G., Borle, M., Shewchuk, T, Hay, S.I. and Snow, R.W. Spatial prediction of Plasmodium falciparum prevalence in Somalia. Malaria J 7:159 doi:10.1186/1475-2875-7-159, 2008.

(30.) Erhart, A, Thang, N.D., Ky, P.V., Tinh, T.T., Overmeir, C.V., Speybroeck, N., Obsomer, V., Hung, L.X., Thuan, L.K., Coosemans, M. and D'alessandro, U. Epidemiology of forest malaria in central Vietnam: a large scale cross-sectional survey. Malaria J4:58 doi: 10.1186/1475-2875-458, 2005.

(31.) Gitongal, C.W., Karanja, P.N., Kihara, J., Mwanje, M., Juma, E., Snow, R.W., Noor, A.M. and Brooker, S. Implementing school malaria surveys in Kenya: towards a national surveillance system. Malaria J 9:306 doi: 10.1186/1475-2875-9-306, 2010.

(32.) Shirayama, Y, Phompida, S. and Shibuyal, K. Geographic information system (GIS) maps and malaria control monitoring: intervention coverage and health outcome in distal villages of Khammouane province, Laos. Malaria J 8:217 doi: 10.1186/1475-2875-8-217, 2009.

(33.) Stresman, G.H., Kamanga, A., Moono, P., Hamapumbu, H., Mharakurwa, S., Kobayashi, T, Moss, W.J. and Shiffl, C. A method of active case detection to target reservoirs of asymptomatic malaria and gametocyte carriers in a rural area in Southern Province, Zambia. Malaria J 9:265 doi: 10.1186/1475-2875-9-265, 2010.

(34.) Haque, U., Hashizume, M., Sunahara, T, Hossain, S., Ahmed, S.M., Haque, R., Yamamoto, T. and Glass, G.E. Progress and challenges to control malaria in a remote area of Chittagong hill tracts, Bangladesh. Malaria J 9:156 doi: 10.1186/1475-2875-9-156, 2010.

(35.) Alba, S., Dillip, A., Hetzel, M.W., Mayumana, I., Mshana, C, Makemba, A., Alexander, M., Obrist, B., Schulze, A., Kessy, R, Mshinda, H. and Lengeler, C. Improvements in access to malaria treatment in Tanzania following community, retail sector and health facility interventions a user perspective. Malaria J 9:163 doi: 10.1186/14752875-9-153, 2010.

(36.) Hetzel, M.W., Alba, S., Fankhauser, M., Mayumana, I., Lengeler, C, Obrist, B., Nathan, R., Makemba, A.M., Mshana, C, Schulze, A. and Mshinda, H. Malaria risk and access to prevention and treatment in the paddies of the Kilombero Valley, Tanzania, Malaria J 7:7 doi: 10. 1186/1475-2875-7-7, 2008.

(37.) Noor, AM., Rage, I.A., Moonen, B. and Snow, R.W. Health service providers in Somalia: their readiness to provide malaria case-management. Malaria J,8:100 doi: 10.1186/1475-2875-7-7, 2009.

(38.) Krishnamurthy, R. Frolov, A., Wolkon, A., Eng, J.V. and Hightower, A. Application of pre-programmed PDA-devices equipped with GPS to conduct paperless household surveys in rural Mozambique. AMIA Symposium, P 999, 2006.

(39.) Centers for Disease Control and Prevention (CDC), USA. Available from PDA/.

(40.) Eng, J.L.V., Thwing, J., Wolkon, A., Kulkarni, M.A., Manya, A., Erskine, M., Hightower, A. and Slutsker, L. Assessing bed net use and non-use after long-lasting insecticidal net distribution: a simple framework to guide programmatic strategies. Malaria J9:133 doi: 10.1186/1475-2875-9-133, 2010.

(41.) Terlouw, D.J., Morgah, K., Wolkon A., Dare, A., Dorkenoo, A, Eliades, M.J., Eng, J.V., Sodahlon, Y.K., Kuile, F.O. and Hawley, W.A. Impact of mass distribution of free long-lasting insecticidal nets on childhood malaria morbidity: The Togo National Integrated Child Health Campaign. Malaria J 9:199 doi: 10.1186/1475-2875-9-199, 2010.

(42.) Oliveira, A.M., Wolkon A., Krishnamurthy, R., Erskine, M., Crenshaw, D.P., Roberts, J. and Saute, F. Ownership and usage of insecticide-treated bed nets after free distribution via a voucher system in two provinces of Mozambique. Malaria J 9:222 doi: 10.1186/1475-2875-9-222, 2010.

(43.) Eng, J.L.V., Wolkon, A., Frolov, AS., Terlouw, D.J., Eliades, M.J., Morgah, K, Takpa, V., Dare, A., Sodahlon, Y.K., Doumanou, Y, Hawley, W.A. and Hightower, A.W. Use of handheld computers with global positioning systems for probability sampling and data entry in household surveys. Am J Trop Med Hyg 77:393, 2007.

(44.) Garmin, what is WAAS? Available from aboutGPS/waas.html.

(45.) Shukla, A.K., Nagori, N., Das, S., Jain, N., Sivaraman, M.R. and Bandyopadhyay, K. Statistical Comparison of Various Interpolation algorithms for grid-based single shell ionospheric model over indian region. J Global Positioning Syst 7:72, 2008

This article has been contributed by Dr. Rekha Saxena, Scientist D, Dr. B.N. Nagpal, Scientist E and Dr. Aruna Srivastava, Emeritus Medical Scientist, National Institute of Malaria Research, New Delhi

Dr. Rekha Saxena, Dr. B.N. Nagpal and Dr. Aruna Srivastava
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Author:Saxena, Rekha; Nagpal, B.N.; Srivastava, Aruna
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Geographic Code:6KENY
Date:Jan 1, 2011
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