• View in gallery

    A map of distribution of community Plasmodium falciparum parasite rate (PfPR) survey data (N = 2604) for the years 2000 to 2010 in Republic of Sudan.

  • View in gallery

    Map of PfPR2–10 malaria endemicity showing the desert fringe, urban settlements, refugee camps, irrigation schemes, and dams.

  • View in gallery

    A map of the probability that a 5 × 5 km location belongs to the endemicity class to which it has been assigned.

  • View in gallery

    Historical expert opinion malaria risk map adapted from Nasr (1968).14 The current State boundaries are shown in light grey and the pre-1970 State boundaries are in black.

  • 1.

    World Health Organization, 1956. The World Health Organization and Malaria Eradication. Geneva: World Health Organization.

  • 2.

    Pampana E, 1969. Textbook of Malaria Eradication. Second edition. Oxford: Oxford University Press.

  • 3.

    Bagster-Wilson D, 1949. Malaria in British Somaliland. East Afr Med J 26: 288.

  • 4.

    Bechuanaland Protectorate, 1959. Annual Medical and Sanitary Report for the Protectorate for the Years 1958. Gaborone: Government Printers.

    • Search Google Scholar
    • Export Citation
  • 5.

    Butler RJ, 1959. Atlas of Kenya: A Comprehensive Series of New and Authenticated Maps Prepared from the National Survey and Other Governmental Sources with Gazetteer and Notes on Pronunciation and Spelling. Nairobi: the Survey of Kenya.

    • Search Google Scholar
    • Export Citation
  • 6.

    Cambournac FJ, Gandara AF, Pena AJ, Teixera WL, 1955. Subsidies for malacology survey in Angola [in Portuguese]. Annals of the Institute of Tropical Medicine (Lisbon) 12: 121152.

    • Search Google Scholar
    • Export Citation
  • 7.

    De Meillon B, 1951. Malaria survey of South-West Africa. Bull World Health Organ 4: 333417.

  • 8.

    Government of Tanganyika, 1956. Atlas of Tanganyika, East Africa. Dar es Salaam: Government Press.

  • 9.

    Government of Uganda, 1962. Atlas of Uganda. Kampala: Department of Lands and Survey.

  • 10.

    Guy Y, Gassabi R, 1967. The outlook for the eradication of malaria in Algeria [in French]. Archives of the Pasteur Institute of Algeria 45: 7288.

    • Search Google Scholar
    • Export Citation
  • 11.

    Hoeul G, Donadille F, 1953. Twenty years of malaria control in Morocco. Bulletin of the Institute of Hygiene Morocco 13: 351.

  • 12.

    Languillon J, 1957. Epidemiological map of malaria in Cameroon [in French]. Bulletin of the Society of Exotic Pathology Exotic and its Subsidiaries 50: 585601.

    • Search Google Scholar
    • Export Citation
  • 13.

    Massa F, 1936. Malaria Somala. G Med Mil 14: 643651.

  • 14.

    Nasr AH, 1968. Preparations for future malaria eradication programme in the Republic of the Sudan. University of Khartoum Faculty of Medicine. J Med Students Assoc 7: 178195.

    • Search Google Scholar
    • Export Citation
  • 15.

    World Health Organization–AFRO, 2012. Manual for Developing a National Malaria Strategic Plan. Brazzaville, Republic of Congo: WHO Regional Office for Africa.

    • Search Google Scholar
    • Export Citation
  • 16.

    Best N, Richardson S, Thomson A, 2005. A comparison of Bayesian spatial models for disease mapping. Stat Methods Med Res 14: 3559.

  • 17.

    Diggle PJ, Ribeiro PJ, 2007. Model-Based Geostatistics. New York: Springer.

  • 18.

    Patil AP, Gething PW, Piel FB, Hay SI, 2011. Bayesian geostatistics in health cartography: the perspective of malaria. Trends Parasitol 27: 246253.

    • Search Google Scholar
    • Export Citation
  • 19.

    Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, Noor AM, Kabaria CW, Manh BH, Elyazar IR, Brooker S, Smith DL, Moyeed RA, Snow RW, 2009. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 6: e1000048.

    • Search Google Scholar
    • Export Citation
  • 20.

    Craig MH, Sharp BL, Mabaso MLH, Kleinschmidt I, 2007. Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure. Int J Health Geogr 6: e44.

    • Search Google Scholar
    • Export Citation
  • 21.

    Gemperli A, Vounatsou P, Sogoba N, Smith T, 2006. Malaria mapping using transmission models: application to survey data from Mali. Am J Epidemiol 163: 289297.

    • Search Google Scholar
    • Export Citation
  • 22.

    Giardina F, Gosoniu L, Konate L, Diouf MB, Perry R, Gaye O, Faye O, Vounatsou P, 2012. Estimating the burden of malaria in Senegal: Bayesian zero-inflated binomial geostatistical modelling of the MIS 2008 data. PLoS ONE 7: e32625.

    • Search Google Scholar
    • Export Citation
  • 23.

    Gosoniu L, Veta AM, Vounatsou P, 2010. Bayesian geostatistical modeling of malaria indicator survey data in Angola. PLoS ONE 5: e9322.

  • 24.

    Gosoniu L, Msengwa A, Lengeler C, Vounatsou P, 2012. Spatially explicit burden estimates of malaria in Tanzania: Bayesian geostatistical modeling of the malaria indicator survey data. PLoS ONE 7: e23966.

    • Search Google Scholar
    • Export Citation
  • 25.

    Kazembe LN, Kleinschmidt I, Holtz TH, Sharp BL, 2006. Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data. Int J Health Geogr 5: e41.

    • Search Google Scholar
    • Export Citation
  • 26.

    Kleinschmidt I, Sharp BL, Clarke CPY, Curtis B, Fraser C, 2001. Use of generalized linear mixed models in the spatial analysis of small-area malaria incidence rates in KwaZulu Natal, South Africa. Am J Epidemiol 153: 12131222.

    • Search Google Scholar
    • Export Citation
  • 27.

    Noor AM, Clements ACA, Gething PW, Moloney G, Borle M, Shewshuk T, Hay SI, Snow RW, 2008. Spatial prediction of Plasmodium falciparum prevalence in Somalia. Malar J 7: 159.

    • Search Google Scholar
    • Export Citation
  • 28.

    Noor AM, Gething PW, Alegana VA, Patil AP, Hay SI, Muchiri E, Juma E, Snow RW, 2009. The risks of Plasmodium falciparum infection in Kenya in 2009. BMC Infect Dis 9: e180.

    • Search Google Scholar
    • Export Citation
  • 29.

    Raso G, Schur N, Utzinger J, Koudou BG, Tchicaya EM, Rohner F, N'Goran EK, Silué ED, Matthys B, Assi S, Tanner M, Vounatsou P, 2012. Mapping malaria risk among children in Cote d'Ivoire using Bayesian geo-statistical models. Malar J 11: 160.

    • Search Google Scholar
    • Export Citation
  • 30.

    Riedel N, Vounatsou P, Miller JM, Gosoniu L, Chizema-Kawesha E, Mukonka V, Steketee RW, 2010. Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS). Malar J 9: 37.

    • Search Google Scholar
    • Export Citation
  • 31.

    Taylor SM, Messina JP, Hand CC, Juliano JJ, Muwonga J, Tshefu AK, Atua B, Emch M, Meshnick SR, 2011. Molecular malaria epidemiology: mapping and burden estimates for the Democratic Republic of the Congo, 2007. PLoS ONE 6: e16420.

    • Search Google Scholar
    • Export Citation
  • 32.

    Snow RW, Alegana VA, Makomva K, Reich A, Uusiku P, Katokele S, Gething PW, Linard C, Tatem AJ, Moonen B, Noor AM, 2010. Estimating the distribution of malaria in Namibia in 2009: assembling the evidence and modelling risk. Ministry of Health & Social Services, Republic of Namibia & Malaria Atlas Project, May 2010.

    • Search Google Scholar
    • Export Citation
  • 33.

    Stensgaard A-S, Vounatsou P, Onapa AW, Simonsen PE, Pedersen EM, Rahbek C, Kristensen TK, 2011. Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co-endemicity. Malar J 10: 298.

    • Search Google Scholar
    • Export Citation
  • 34.

    Petrarca V, Nugud AD, Ahmed MA, Haridi AM, Di Deco MA, Coluzzi M, 2000. Cytogenetics of the Anopheles gambiae complex in Sudan, with special reference to An. arabiensis: relationships with East and West African populations. Mel & Vet Ento 14: 149164.

    • Search Google Scholar
    • Export Citation
  • 35.

    Lewis DJ, 1956. The anopheline mosquitoes of the Sudan. Bull Entomol Res 47: 475494.

  • 36.

    Zahar AR, 1985. Vector Bionomics in the Epidemiology and Control of Malaria. Part I.III. East Africa. Geneva: World Health Organization, WHO/VBC/85.3.

    • Search Google Scholar
    • Export Citation
  • 37.

    El Gaddal AA, 1985. The Blue Nile Health Project: a comprehensive approach to the prevention and control of water-associated diseases in irrigated schemes of the Sudan. J Trop Med Hyg 88: 4756.

    • Search Google Scholar
    • Export Citation
  • 38.

    Kempinska-Podhorodecka A, Knap O, Drozd A, Kaczmarczyk M, Parafiniuk M, Parczewski M, Ciechanowicz A, 2012. Analysis for genotyping Duffy blood group in inhabitants of Sudan, the Fourth Cataract of the Nile. Malar J 11: 115.

    • Search Google Scholar
    • Export Citation
  • 39.

    Wernsdorfer G, Wernsdorfer W, 1967. Malaria in the middle Nile basin and its bordering regions. Z Tropenmed Parasitol 18: 1744.

  • 40.

    Balfour A, 1913. A year's anti-malarial work at Khartoum. J Trop Med Hyg 14: 227232.

  • 41.

    Kushkush HA, 1968. Malaria in The Sudan: malaria, general background, history and its history in The Sudan. University of Khartoum Faculty of Medicine. J Med Students Assoc 7: 105110.

    • Search Google Scholar
    • Export Citation
  • 42.

    Salih H, Idris A, 1968. Malaria in The Sudan: malaria prevention and control. University of Khartoum Faculty of Medicine. J Med Students Assoc 7: 159168.

    • Search Google Scholar
    • Export Citation
  • 43.

    World Health Organization-Sudan, 1956. Information on the Malaria Control Programme in the Sudan. WHO EMRO inter-regional meeting, April 1956. World Health Organization, Athens Meeting, WHO/MAL/163-14.

    • Search Google Scholar
    • Export Citation
  • 44.

    El Gaddal AA, 1968. Malaria in The Sudan: malaria pilot project Sudan 6 (1956–1960). University of Khartoum Faculty of Medicine. J Mel Students Assoc 7: 197200.

    • Search Google Scholar
    • Export Citation
  • 45.

    Henderson LH, 1934. Prophylaxis of malaria in The Sudan with special reference to plasmoquine. Trans R Soc Trop Med Hyg 28: 157164.

  • 46.

    Sudan National Malaria Administration, 1998. Blue Nile Health Project, Annual Report. Ministry of Health Sudan.

  • 47.

    Mirghani ES, Nour BY, Bushra SM, Hassan El I, Snow RW, Noor AM, 2010. The spatial-temporal clustering of Plasmodium falciparum infection over eleven years in Gezira State, The Sudan. Malar J 9: e172.

    • Search Google Scholar
    • Export Citation
  • 48.

    Federal Ministry of Health, 2004. Documentation of Khartoum and Gezira Malaria Initiatives. Khartoum: Federal Ministry of Health, 133.

    • Search Google Scholar
    • Export Citation
  • 49.

    Khalifa SM, Mustafan IO, Wais M, Malik EM, 2008. Malaria control in an urban area; a successful story from Khartoum, 1995–2004. La Revue de Santé de la Méditerranée Orientale 14: 206215.

    • Search Google Scholar
    • Export Citation
  • 50.

    Malik EM, Ahmed ES, Elkhalifa SM, Hussein MA, Suleiman AMN, 2003. Stratification of Khartoum urban area by the risk of malaria transmission. East Mediterr Health J 9: 559569.

    • Search Google Scholar
    • Export Citation
  • 51.

    Shousha AT, 1948. The eradication of A. gambiae from Upper Egypt, 1942–1945. Bull World Health Organ 1: 309348.

  • 52.

    Farid MA, 1984. A mission to the Gambiae Project area in Egypt and Sudan. 13 February to 15 March 1984. World Health Organization, WHO-EM/MAL/204.

    • Search Google Scholar
    • Export Citation
  • 53.

    Mashaal HAH, Dukeen MYH, Zarroug IMA, 1988. Assessment of malaria in Sennar sugar project: 7–18 February 1987. Report by Independent In-depth Review Team to WHO-EMRO.

    • Search Google Scholar
    • Export Citation
  • 54.

    El Gaddal AA, 1986. Malaria in the Sudan. Proceedings of the Conference on Malaria in Africa. Practical considerations on malaria vaccine and clinical trials. Washington, DC, December 1–4, 156159.

    • Search Google Scholar
    • Export Citation
  • 55.

    Malik EM, Atta HY, Weis M, Land A, Puta C, Lettenmaier C, Bell A, 2004. Sudan Roll Back Malaria consultative mission essential actions to support the attainment of the Abuja targets. 16–20 November 2003. SUDAN RBM Country Consultative Mission Final Report.

    • Search Google Scholar
    • Export Citation
  • 56.

    Federal Ministry of Health - National Malaria Control Programme, 2006. National Malaria Strategic Plan 2007–2012. Khartoum.

  • 57.

    Federal Ministry of Health - National Malaria Control Programme, 2010. Five Years Strategic Plan for the National Malaria Control Programme Sudan, 2011–2015. Khartoum.

    • Search Google Scholar
    • Export Citation
  • 58.

    Shililu JI, Grueber WB, Mbogo CM, Githure JI, Riddiford LM, Beier JC, 2004. Development and survival of Anopheles gambiae eggs in drying soil: influence of the rate of drying, egg age, and soil type. J Am Mosq Control Assoc 20: 243247.

    • Search Google Scholar
    • Export Citation
  • 59.

    Gray EM, Bradley TJ, 2005. Physiology of desiccation resistance in Anopheles gambiae and Anopheles arabiensis. Am J Trop Med Hyg 73: 553559.

    • Search Google Scholar
    • Export Citation
  • 60.

    MODIS-EVI data archives, 2005. Available at: ftp://n4ftl01u.ecs.nasa.gov/SAN/MOST/MOD10A2.005/. Accessed 15 June, 2011.

  • 61.

    Guerra CA, Snow RW, Hay SI, 2006. Defining the global limits of malaria transmission in 2005. Adv Parasitol 62: 157179.

  • 62.

    Guerra CA, Gikandi PW, Tatem AJ, Noor AM, Smith DL, Hay SI, Snow RW, 2008. The limits and intensity of Plasmodium falciparum: implications for malaria control and elimination worldwide. PLoS Med 5: e38.

    • Search Google Scholar
    • Export Citation
  • 63.

    Balk D, Pozzi F, Yetman G, Deichmann U, Nelson A, 2004. The Distribution of People and the Dimension of Place: Methodologies to Improve Global Population Estimates in Urban and Rural Areas. New York: CIESIN Columbia University.

    • Search Google Scholar
    • Export Citation
  • 64.

    Balk D, Pullum T, Storeyguard A, Greenwell F, Neuman M, 2004. A spatial analysis of childhood mortality in West Africa. Popul Space Place 10: 175216.

    • Search Google Scholar
    • Export Citation
  • 65.

    Global Lakes and Wetlands Database, 2004. Available at: https://secure.worldwildlife.org/science/data/item1877.html. Accessed June 10, 2011.

    • Search Google Scholar
    • Export Citation
  • 66.

    Wallach B, 2004. Irrigation in Sudan since Independence. Geogr Rev 74: 127144.

  • 67.

    United Nations High Commission for Refugees, 2012. Sudan. Available at: http://www.unhcr.org/pages/49e483b76.html. Accessed 20 March, 2012.

    • Search Google Scholar
    • Export Citation
  • 68.

    Federal Ministry of Health - National Malaria Control Programme, 2005. Malaria Prevalence and Coverage Indicators Survey Sudan – October 2005. Final Report, December 2005.

    • Search Google Scholar
    • Export Citation
  • 69.

    Federal Ministry of Health - National Malaria Control Programme, 2010. Malaria Indicator Survey 2009 in the Northern States of the Sudan. Final Report, March 2010.

    • Search Google Scholar
    • Export Citation
  • 70.

    Nourien AB, Abass MA, Abdel Nugud AD, El Hassan I, Snow RW, Noor AM, 2011. Identifying residual foci of Plasmodium falciparum infections for malaria elimination: the urban context of Khartoum, Sudan. PLoS ONE 6: e16948.

    • Search Google Scholar
    • Export Citation
  • 71.

    Federal Ministry of Health, Sudan, 2009. Malaria Parasitology Survey Reports of Gambiae project, 1999–2009. Sudan: Federal Ministry of Health.

    • Search Google Scholar
    • Export Citation
  • 72.

    Smith DL, Guerra CA, Snow RW, Hay SI, 2007. Standardizing estimates of malaria prevalence. Malar J 6: e131.

  • 73.

    Malaria Atlas Project, 2010. P. falciparum Cartographic code. Available at: https://github.com/malaria-atlas-project/mbgw-clean. Accessed March 10, 2011.

    • Search Google Scholar
    • Export Citation
  • 74.

    Noor AM, Alegana VA, Moloney G, Borle M, Ahmed F, Yousef F, Amran J, Snow RW, 2012. Mapping the receptivity of malaria risk to plan the future of control in Somalia. BMJ Open 2: e001160.

    • Search Google Scholar
    • Export Citation
  • 75.

    Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A, 2005. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25: 19651978.

    • Search Google Scholar
    • Export Citation
  • 76.

    Gething PW, Van Boeckel T, Smith DL, Guerra CA, Patil AP, Hay SI, 2011. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasit Vector 4: 92.

    • Search Google Scholar
    • Export Citation
  • 77.

    Miller A, 2002. Subset Selection in Regression. Boca Raton, FL: Chapman & Hall.

  • 78.

    Lumley T, 2010. Leaps: regression subset selection (R package) version 2.7.

  • 79.

    Schwarz G, 1978. Estimating dimensions of a model. Ann Stat 6: 461464.

  • 80.

    Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ, 2012. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE 7: e31743.

    • Search Google Scholar
    • Export Citation
  • 81.

    The AfriPop Project, 2009. Available at: http://www.clas.ufl.edu/users/atatem/index_files/Details.htm. Accessed March 12, 2012.

  • 82.

    UNHCR, 2012. Country Operations Profile - Sudan. Available at: http://www.unhcr.org/pages/49e483b76.html. Accessed May 12, 2010.

  • 83.

    WHO, 2005. Malaria Control in Complex Emergencies: An Interagency Field Handbook. http://whqlibdoc.who.int/publications/2005/924159389X_eng.pdf. Accessed May 3, 2012.

    • Search Google Scholar
    • Export Citation
  • 84.

    Najera JA, 1996. Malaria Control among Refugees and Displaced Populations. World Health Organization, Division of Control of Tropical Diseases, Malaria Unit.

    • Search Google Scholar
    • Export Citation
  • 85.

    UNHCR, 2008. Strategic Plan for Malaria Control 2008–2012. Available at: http://www.unhcr.org/488597e02.html. Accessed May 14, 2012.

  • 86.

    De Castro MC, Yamagata Y, Mtasiwa D, Tanner M, Utzinger J, Keiser J, Singer BH, 2004. Integrated urban malaria control: a case study in Dar Es Salaam, Tanzania. Am J Trop Med Hyg 71: 103117.

    • Search Google Scholar
    • Export Citation
  • 87.

    Fillinger U, Kannady K, William G, Vanek MJ, Dongus S, Nyika D, Geissbühler Y, Chaki PP, Govella NJ, Mathenge EM, Singer BH, Mshinda H, Lindsay SW, Tanner M, Mtasiwa D, Castro MC, Killeen GF, 2008. A tool box for operational mosquito larval control: preliminary results and early lessons from the Urban Malaria Control Programme in Dar es Salaam, Tanzania. Malar J 7: 20.

    • Search Google Scholar
    • Export Citation
  • 88.

    Sudan Medical Service, 1931. Report on Medical and Health Work in the Sudan for the Year 1930. (Sudan) Limited Khartoum: McCorquodale & Co.

    • Search Google Scholar
    • Export Citation
  • 89.

    Elgaddal 1991. Malaria in the Sudan. Malaria and Development in Africa: a Cross-Sectional Approach. A report by the American Association for the Advancement of Science.

    • Search Google Scholar
    • Export Citation
  • 90.

    Omumbo JA, Noor AM, Fall IS, Snow RW, 2012. How Well is Malaria Risk Cartography Used to Design and Finance Malaria Control in Africa? Report prepared for DFID-UK and partners, June 2012.

    • Search Google Scholar
    • Export Citation
  • 91.

    WHO 2008. Global Malaria Control and Elimination: Report of a Technical Review. Available at: http://www.who.int/malaria/publications/atoz/9789241596756/en/index.html. Accessed June 10, 2012.

    • Search Google Scholar
    • Export Citation
  • 92.

    Smith DL, Noor AM, Hay SI, Snow RW, 2009. Predicting changing malaria risk following expanded insecticide treated net coverage in Africa. Trends Parasitol 25: 511516.

    • Search Google Scholar
    • Export Citation
  • 93.

    Griffin JT, Hollingsworth TD, Okell LC, Churcher TS, White M, Hinsley W, Bousema T, Drakeley CJ, Ferguson NM, Basáñez MG, Ghani AC, 2010. Reducing Plasmodium falciparum malaria transmission in Africa: a model-based evaluation of intervention strategies. PLoS Med 7: e1000324.

    • Search Google Scholar
    • Export Citation
  • 94.

    Snow RW, Marsh K, 2002. The consequences of reducing Plasmodium falciparum transmission in Africa. Adv Parasitol 52: 235264.

  • 95.

    Snow RW, Marsh K, 2010. Malaria in Africa: progress and prospects in the decade since the Abuja Declaration. Lancet 376: 137139.

  • 96.

    Drakeley CJ, Corran PH, Coleman PG, Tongren JE, McDonald SL, Carneiro I, Malima R, Lusingu J, Manjurano A, Nkya WM, Lemnge MM, Cox J, Reyburn H, Riley EM, 2005. Estimating medium- and long-term trends in malaria transmission by using serological markers of malaria exposure. PNAS USA 102: 51085113.

    • Search Google Scholar
    • Export Citation
  • 97.

    Noor AM, Mohamed MB, Mugyenyi C, Osman MA, Guessod HH, Kabaria CW, Nyonda M, Cook J, Drakelely CJ, Mackinnon MJ, Snow RW, 2011. Establishing the extent of malaria transmission and challenges facing pre-elimination in the Republic of Djibouti. BMC Infect Dis 11: 121.

    • Search Google Scholar
    • Export Citation
  • 98.

    Bejon P, Liljander A, Noor AM, Wambua J, Ogada E, Olotu A, Osier F, Hay S, Färnert A, Marsh K, 2010. Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. PLoS Med 7: e1000304.

    • Search Google Scholar
    • Export Citation
  • 99.

    Bousema T, Griffin JT, Sauerwein RW, Smith DL, Churcher TS, Taken W, Ghani A, Drakeley C, Gosling R, 2012. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med 9: e1001165.

    • Search Google Scholar
    • Export Citation
  • 100.

    Rue H, Martino S, 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc, B 71: 319392.

    • Search Google Scholar
    • Export Citation
  • 101.

    Roll Back Malaria, 2008. The Global Malaria Action Plan. Roll Back Malaria partnership. Geneva: World Health Organization.

  • 102.

    Cohen JM, Moonen B, Snow RW, Smith DL, 2010. How absolute is zero? An evaluation of historical and current definitions of malaria elimination. Malar J 9: 213.

    • Search Google Scholar
    • Export Citation
 
 
 
 

 

 
 

 

 

 

 

 

 

Malaria Risk Mapping for Control in the Republic of Sudan

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  • Malaria Public Health Theme, Centre for Geographic Medicine Research, Coast, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya; Centre for Tropical Medicine, Nuffield Department of Clinical Medicine, University of Oxford, United Kingdom; National Malaria Control Programme, Federal Ministry of Health, Republic of Sudan; Sense Inc., Detroit, Michigan; Institute of Endemic Diseases, Department of Parasitology, University of Khartoum, Khartoum, Sudan

Evidence shows that malaria risk maps are rarely tailored to address national control program ambitions. Here, we generate a malaria risk map adapted for malaria control in Sudan. Community Plasmodium falciparum parasite rate (PfPR) data from 2000 to 2010 were assembled and were standardized to 2–10 years of age (PfPR2–10). Space-time Bayesian geostatistical methods were used to generate a map of malaria risk for 2010. Surfaces of aridity, urbanization, irrigation schemes, and refugee camps were combined with the PfPR2–10 map to tailor the epidemiological stratification for appropriate intervention design. In 2010, a majority of the geographical area of the Sudan had risk of < 1% PfPR2–10. Areas of meso- and hyperendemic risk were located in the south. About 80% of Sudan's population in 2011 was in the areas in the desert, urban centers, or where risk was < 1% PfPR2–10. Aggregated data suggest reducing risks in some high transmission areas since the 1960s.

Background

During the era of the Global Malaria Eradication Program (GMEP) it was recognized that the paradigm of “one size fits all” for the selection of appropriate interventions would not work. Countries were encouraged to develop a reconnaissance of their malaria epidemiology that included mapping the intensity of transmission, distribution of dominant vectors, and epidemiological features important for local transmission, including population settlement, rivers, dams, and agricultural areas.1,2 Maps developed by national malaria control agencies during the GMEP varied in the information used but most were based on the association between rainfall duration and malaria seasons, altitude, proximity to breeding sites, and occasionally supported by empirical observations of incidence and prevalence of malaria.314 There was an obvious appetite for risk mapping over 60 years ago and a sense that these were important national atlases to guide disease control. The science and effort to mount malaria cartography across much of Africa diminished when the regional control agenda shifted from one of preventing infection to treating fevers in the late 1970s.

In 2011, the World Health Organization (WHO) Office for the Africa Region (AFRO) developed a manual to assist countries in developing their National Malaria Strategic (NMS) plans including, as a prelude, the undertaking of a National Malaria Program Performance Review (MPR).15 The MPR should include a detailed review of the malaria epidemiology and stratification including the geographical distribution of malaria burden, parasite prevalence, and parasite species. This renewed plea for national malaria risk mapping coincides with a time when the international donor community is constrained by the global financial crisis. Accessing overseas development assistance and national domestic funding for malaria control will require a much stronger evidence-based business case to define the needs of control and elimination sub-nationally to allocate limited financial resources more efficiently.

Over the last 15 years there has been a proliferation in the co-availability of 1) national, geo-coded parasite prevalence data; 2) spatially interpolated climate data derived from ground station observations; and 3) remotely sensed satellite surrogates of climate, urbanization, and topography. Advances in computing speeds and model-based geostatistical (MBG) techniques have increased our ability to define the spatial risks of malaria endemicity using probabilistic approaches at high spatial resolutions.1618 These advances have underpinned approaches to defining the global patterns of malaria transmission intensity19 and spurred a renaissance in malaria risk mapping at country-levels.2033 Today, National Malaria Control Programs have access to a range of state of the art mapping products that might serve their planning needs. However, each country has specific epidemiological and intervention needs that must be accommodated with standard approaches to malaria risk mapping to provide adequate planning information.

Here, we examine the use of MBG applied to nationally assembled malariometric data in the Republic of Sudan to define the contemporary spatial intensity of Plasmodium falciparum transmission and use other remotely sensed data to define additional epidemiological strata important for sub-national malaria control. We discuss the applications of this map for the future of malaria control in the Sudan and compare the descriptions of malaria risk today with historical GMEP definitions 50 years earlier.

Methods

Country context.

Malaria transmission is maintained almost entirely by Anopheles arabiensis Paton across all of the Republic of Sudan. There are possible foci of Anopheles gambiae s.s. transmission but these are considerably rarer; in only one of 50 sites surveyed in the Republic of Sudan, at Sennar, was An. gambiae s.s. identified.34 Both Anopheles nili (Theobold)35 and Anopheles pharoensis (Theobold)36 have been reported but neither are thought to contribute to transmission.34,37 Over 90% of all infections are with P. falciparum; despite the presence of duffy-receptive populations,38 Plasmodium vivax is rare.39

The Sudan has a rich history of malaria control dating back over 100 years with the establishment of “mosquito sanitary workers” who maintained house-screening, larviciding, and engineering works to mitigate against the seasonal flooding of the Nile.4042 Indoor residual spraying (IRS) using dichlorodiphenyltrichloroethane (DDT) was first introduced in 1946 and expanded to rural areas by 1951 following pilot success in larger towns. The areas of focal control included Fung District (Blue Nile Province), Kordofan, and Darfur Provinces. From the mid-1950s DDT was increasingly replaced by Gammaxene. The IRS and larval control were gradually extended to smaller towns and the cotton agriculture areas south of Khartoum. In the 1950s chemoprophylaxis was only reportedly used in school children, the armed forces, and the police force.43 By 1970 the WHO regarded Sudan to be in the attack phase of elimination and the government divided the country into three zones to shrink the malaria map under the aegis of the Malaria Eradication Service supported by the Malaria Eradication Training Center at Sennar.44 However, this did not translate into a definitive and implemented national elimination program. Over the next 30 years there were a number of focal malaria elimination projects including: targeted projects along the banks of the Blue Nile as part of the Gezira Irrigation scheme that began in the 1930s,42,45 expanded during the 1970s and 1980s,37,46,47 and resurrected as the Gezira Malaria Free Initiative in 199947,48; sustained urban malaria control from the 1970s until the late 1990s when the Khartoum Malaria Free Initiative (KMFI) was launched in response to rising epidemics in the 1990s49,50; the long-term collaborative project between the Governments of Egypt and Sudan at Wadi Halfa started in 1948 to prevent the “invasion” of An. gambiae into Egypt51 from the Wadi Halfa region sustained since the 1950s52; projects at the Sennar Sugar factories53; Roseires Dam and expanded control in Blue Nile region to include the Rahad irrigation scheme completed in 1983.54

In concert with tackling the pan-African epidemic, Sudan launched its first national malaria strategy in support of the Roll Back Malaria initiative soon after 2001. As with much of sub-Saharan Africa the focus of control during the early 2000s was on increasing coverage of insecticide-treated net (ITN) distributions, targeted IRS and larval control, intermittent presumptive treatment of pregnant women, and improved malaria case management, including replacing chloroquine with artesunate + sulphadoxine pyrimethamine in 2004.55,56 In 2007 a revised national strategy was launched that had as its vision a 50% reduction of malaria-related morbidity and mortality by 2012.57 This strategic plan recognized the diversity of malaria risks across the Republic and tailored priority interventions accordingly including specific elimination ambitions in targeted areas (Khartoum and Gezira).57 Of the important spatially defined risk groups the strategic plan identifies large urban settlements outside of Khartoum and refugee populations that have transformed epidemiological risks and have posed challenges to malaria control since the 1980s when people began to arrive, and settle from Eritrea, Ethiopia, Chad, the Democratic Republic of the Congo and Somalia and internally displaced populations resulting from the North-South Sudan and the Darfur conflicts.

Defining the limits of transmission based on aridity.

Arid conditions play an important role in determining anopheline development and survival.58 Limited surface water reduces the availability of sites suitable for oviposition and reduces the survival of vectors at all stages of their development through the process of desiccation.59 The Sahara Desert covers the majority of the northern parts of the Sudan ecologically constraining malaria transmission in this area. To define aridity, freely available enhanced vegetation index (EVI) at 1 × 1 km spatial resolution processed from earth orbiting satellite imagery60 was used. Data from synoptic 12 monthly mean surfaces for the period 2001–2010 were used to classify areas of the Republic into those likely to support transmission, defined by an EVI of > 0.1 for any two consecutive months and areas without two or more consecutive months of an EVI > 0.1 as unable to support transmission.61,62

Defining special human settlement areas targeted for control.

Urban area extents of the Sudan were extracted from the Global Rural Urban Mapping Project (GRUMP).63,64 The location and extents of irrigation schemes and dams were delimited using the location of important water bodies defined by the National Malaria Control Program57 and triangulated using other sources.65,66 Maps of the official locations of internally displaced people and international refugees were obtained from the United Nations High Commission for Refugee Sudan webpage67 and improved using details in the national malaria strategy.57 These images were digitized and displayed in ArcGIS 10.

Modeling the intensity of P. falciparum transmission.

Community P. falciparum parasite rate (PfPR) data were assembled from cross-sectional surveys undertaken between January 1, 2000 and December 31, 2010. These included national sample survey data from the 2005 and 2009 malaria indicator surveys (MIS). The 2005 MIS survey was undertaken in October 2005 and covered 115 randomly selected clusters across eight States of Sudan and included the parasitological examination of 3,771 children 2–10 years of age using microscopy.68 A second national sample survey was undertaken between October to December 2009 covering 300 community clusters and recorded infection prevalence using Rapid Diagnostic Tests (First Response Combo, Premier Medical Corporation Ltd., India) among 21,988 individuals of whom 13,846 were children 2–10 years of age.69 Clusters surveyed during both MIS were drawn from a national sampling frame of enumeration areas. Additional survey data were obtained from surveys within the project areas of Khartoum,70 Gezira,47 and Wadi Halfa71 and other research projects undertaken during the observation period as part of peer-reviewed publications or post-graduate theses. All survey locations were geo-located using combinations of national and web-based digital place name directories (Figure 1).

Figure 1.
Figure 1.

A map of distribution of community Plasmodium falciparum parasite rate (PfPR) survey data (N = 2604) for the years 2000 to 2010 in Republic of Sudan.

Citation: The American Society of Tropical Medicine and Hygiene 87, 6; 10.4269/ajtmh.2012.12-0390

The assembled P. falciparum parasite prevalence data were reported across different age groups and were re-classified to the classical age range of 2 to < 10 years of age using an algorithm based on modified catalytic conversion models.72 The age-standardization algorithm computes the influence of age on the probability of detecting infection at a given cluster location, which by extension is a function of the underlying transmission of that cluster.18 Continuous surfaces of the age-standardized data (PfPR2–10) were generated using a space-time Bayesian geostatistical framework73 described in detail elsewhere18,28,74 and implemented using the Markov Chain Monte Carlo algorithm. The value of PfPR2–10 was modeled as a transformation of a spatiotemporally structured field superimposed with unstructured (random) variation on a regular 5 × 5 km grid from 2005 and 2010. The number of P. falciparum positive responses from the total sample at each survey location was modeled as a conditionally independent binomial variate given the unobserved underlying PfPR2–10 and a linear function of climatic and environmental predictors.

The environmental covariates that were considered were synoptic annual average EVI60 and precipitation,75 urbanization,64 temperature suitability index for malaria transmission76 and distance from the Nile River and major irrigation schemes resampled to 5 × 5 km grids. The values of the underlying ecological and climatic covariates were extracted to each survey location using ArcGIS 10 Spatial Analyst tool. Distance to the Nile River and major irrigation schemes was log-transformed before analysis because of its high positive skew. The covariates were then included in total-sets analysis, which is an automatic model selection process based on a generalized linear regression model and implemented using the bestglm package in R.77,78 This approach selects the best combination of the covariates based on the value of the Bayesian information criteria (BIC) statistic,79 which selects the set of predictors with the lowest BIC as the best model fit.

For each 5 × 5 km grid location samples of the annual mean of the full posterior distribution of PfPR2–10 for each year were generated. The full posterior distribution of PfPR2–10 was then used to generate the following malaria endemicity classes: PfPR2–10 < 1%; 1–10%; ≥ 10–50%; and >50%. A spatially representative 10% holdout dataset was used for model validation and measures of model uncertainty included the mean prediction error (MPE) and the mean absolute prediction error (MAPE). The probability of membership of a survey location to its assigned endemicity class was also computed as a further measure of uncertainty. These probabilities, ranging from 0.25 (membership equally likely to all classes) to 1 (no uncertainty in class membership) were computed from the posterior distributions resulting from the Bayesian geostatistical model.

The PfPR2–10 risk classification was combined with the maps of aridity, urbanization, refugee camps, and irrigation and dams to create an adapted map of malaria stratification for the Sudan. Within each risk strata the projected population at risk in 2011 was extracted from a high-resolution population grid map.80,81 Population data for refugee camps were obtained from recent estimates of the United Nations High Commission for Refugees (UNHCR)82 and are not necessarily specific to the year 2010. A combination of strategic approaches defined within the NMS 2011–2015,57 the WHO and UNHCR guidelines on malaria control among refugees8385 and literature on the control of malaria in urban areas86,87 and documented practices in both the urban areas of Khartoum and Gezira Malaria Free Initiatives47,70 were then used together with the PfPR2–10 predictions to adapt epidemiologically appropriate interventions for each malaria risk stratum.

Comparing 2010 malaria transmission with 1960.

Malaria risk stratification in Sudan began in the 1930s based largely on latitude and proximity to the seasonal rise and fall of the Nile.88 In the early 1960s the WHO recommended that a national pre-eradication survey be undertaken across the whole of Sudan to support plans for a country-wide malaria eradication program.14,39 The survey was undertaken from 1961 to 1963 and included spleen and parasite rates, detailed descriptions of seasonality, topography, and assembled health center morbidity and mortality reports and its rationale resonates with calls 50 years later by the WHO to improve current malaria intelligence.15 The surveys during the GMEP arguably provided a richer set of epidemiological data compared with most contemporary malaria indicator surveys. Despite repeated attempts to locate the sampled village-level data from this 1960s survey at archives across the Sudan these raw data appear to have been lost or located outside of the country. It was therefore not possible to compare directly the 1960s data using identical methods to those used to predict risks in 2010. Examination of summaries of the national sample survey estimates of infection prevalence in 1961–63, 2005, and 2010 were instead undertaken across the previously defined state boundaries of Northern, Red Sea, River Nile, Khartoum, and Kassala. It was not possible for the malariologists of the time to use modern geostatistical methods to quantitatively define endemicity at high spatial resolutions. As such only semi-quantitative maps were produced to assist in prioritizing malaria control during the GMEP era.14 This 1960s malaria risk map was digitized and displayed at a scale similar to the contemporary risk map to allow for a more direct comparison.

Results

Predicting malaria transmission intensity in 2010.

A total of 2,604 community PfPR survey clusters from 913 unique locations for the period 2000–2010 were used to predict at unsampled 5 × 5 km grid locations to the year 2010 using the Bayesian space-time geostatistical model. The results of the total-set analysis showed that the model with urbanization, precipitation, and EVI as the best fit in predicting PfPR and these variables were subsequently included in the malaria prediction model. The binned categories of the predicted endemicity are shown in Figure 2 including the spatial delineation of special epidemiological populations living in major urban centers, irrigation zones and refugee settlements. The probability of class membership was > 0.25 indicating better than chance classifications of endemicity throughout (Figure 3). However, the probabilities were lowest in the higher risk areas in the south of the country where risk is very heterogeneous and with sparse data distribution. The overall MPE and MAPE were −1.4% and 0.01%, respectively, and area under the curve values of 0.70 indicating overall good model accuracy.

Figure 2.
Figure 2.

Map of PfPR2–10 malaria endemicity showing the desert fringe, urban settlements, refugee camps, irrigation schemes, and dams.

Citation: The American Society of Tropical Medicine and Hygiene 87, 6; 10.4269/ajtmh.2012.12-0390

Figure 3.
Figure 3.

A map of the probability that a 5 × 5 km location belongs to the endemicity class to which it has been assigned.

Citation: The American Society of Tropical Medicine and Hygiene 87, 6; 10.4269/ajtmh.2012.12-0390

The refined matrix of malaria eco-epidemiology and control interventions is presented in Table 1. The aridity constrained malaria-free “Desert Fringe” areas constitute 1.1 million km2 (or 59% of Sudan's land mass) and were occupied by 4.5 million out of the 31 million in Sudan people in 2011. The areas on the margins of the Desert Fringe classified as low stable endemic control (PfPR2–10 < 1%) cover almost all of the states of Northern, Red Sea, River Nile, and Khartoum and large areas of states further south (Figure 2) covering ∼8.2 million people or 26.5% of the total population in 2011 (Table 1). The hypoendemic class of ≥ 1 but < 10% PfPR2–10 was predicted across the states of Northern Darfur, Northern Kordofan, White Nile, Gezira, Kassala and parts of the southern states encompassing 3.4 million people, 11% of the 2011 population. The mesoendemic class with pockets of hyperendemicity is located mainly in areas between the latitude 12° and the border with South Sudan and inhabited by an estimated 3.4 million people. The urban areas in Sudan made up of the capital city Khartoum, all the state capitals and other major towns contained 8.4 (27%) million people exposed to largely very low malaria risks. The irrigation schemes covered a population of 3.1 (10%) million, whereas a total of 3.2 million people were estimated to live in refugee camps in the Sudan. Although some of the refugees were from neighboring countries, it is expected that most are internally displaced people as a result of the Darfur conflict or tribal skirmishes along the border with South Sudan. Estimates of the refugee population were computed separately from the overall analysis of the proportion of population in different malaria strata in the Sudan.

Table 1

Adapted and revised malaria risk classifications, targeted areas, population, and interventions recommended for adoption for malaria control in the Republic of Sudan in 2011

Strata: transmission levelsAreasPopulation in millions (%)Main control interventions*
Desert Fringe: Malaria freeMajority of areas in the North above latitude 15°4.5 (14.5)- Case surveillance, detection, and investigation.
Low stable endemic control: outside the aridity mask but < 1% PfPR2–10Focal areas in the Northern, River Nile, and Red Seas states, rural areas in Khartoum, southern parts of North Darfur, northern parts of South, and West Darfur, southern parts of North Kordofan, northern parts of South Kordofan Blue Nile, White Nile, Sennar, Gezira, Gedaref, and Kassala.8.2 (26.5)- Case surveillance
- Entomological surveillance
- Larval control
- Spatially targeted IRS
- Epidemic early warning, early detection, and rapid response.
Hypoendemic: outside the aridity mask but 1–10% PfPR2–10Other rural areas in Greater Darfur, Kordofan, Blue Nile, White Nile, Sennar Gezira Gedaref, Kassala, Khartoum states3.4 (11.0)- Spatially targeted IRS coverage
- Spatially targeted LLIN coverage
- Epidemic early warning, detection and rapid response
Largely mesoendemic transmission with pockets of hyperendemicity: > 10% PfPR2–10Southern parts of South Darfur, West Darfur, South Kordofan; most of Blue Nile; eastern parts of Sennar and Gedaref3.4 (11.0)- Universal LLIN coverage
- Epidemic early warning, detection, and rapid response with targeted IRS as a supplementary intervention
Urban malaria: Cuts across all endemicities but generally low because of urbanization and/or ecologyKhartoum and all large cities and state capitals8.4 (27.0)- Case surveillance
- Entomological surveillance
- Source reduction where appropriate (with community involvement)
- Larval control
- IRS during threat of epidemics
Irrigated schemes and major dams: Mainly along the Nile Rivers risks mainly hypoendemic because of control but with remaining small areas of mesoendemic transmissionAll large-scale irrigated schemes (Gezira, Elrahad, Kinana, Asalia, West Sinnar, New Halafa, and Elzidab)3.1 (10.0)- Entomological surveillance
- Targeted IRS
- Larviciding
- LLIN coverage in areas where baseline transmission is >1% PfPR2–10
Emergency and complex situation: Risk dependent of original and destination of incoming individuals (i.e., immunity and receptivity); usually epidemic prone.Mainly internally displaced populations (IDP) camps in North, West, and South Darfur; Refugee camps in Khartoum, Gedaref, and Kassala states.3.2- Rapid screening of incoming populations
- Surveillance, preferably integrated with other disease information systems
Estimates were derived from data at various time points between 2007 and 2010.- Source reduction
- LLIN coverage in areas where receptive transmission is > 1% PfPR2–10

All malaria strata include effective case-management as an intervention.

To match appropriate interventions to a given epidemiological stratum various reference sources were used for: urban areas47,70,86,87; refugee camps and internally displaced persons settlements 8385; irrigation schemes,37,57 and for all other strata.57,91,101

Low stable endemic areas are defined as those where PfPR2–10 are < 1% and are considered to be areas where it is technically feasible to undertake malaria elimination.102

PfPR2–10 = Plasmodium falciparum parasite rate; IRS = indoor residual spraying; LLIN = long-lasting insecticidal nets.

Comparing 2010 malaria transmission with 1960.

The 1960s survey covered 24,373 children between 2 and 10 years of age in all the previous states that now form the Republic of Sudan (Table 2). The product of this survey data and informed expert opinion, based on proximity to the Nile, rainfall patterns, and deserts is a map shown in Figure 4. Striking is the broad consensus on the spatial distribution of endemicity risk classifications today (Figure 2) and 50 years previously (Figure 4), with the exception of the meso- and hyper-endemic classes that appear wider and more prolific 50 years ago. The examination of summaries of the national sample survey estimates of infection prevalence 1961–63, 2005, and 2010 suggests that across the previously defined state boundaries of Northern, Red Sea, River Nile, Khartoum, and Kassala, PfPR2–10 years has remained consistently low over the last 50 years (Table 2). In the southern states of Darfur and Kordofan there have been a long-term decline and some evidence of a more rapid decline since 2005; interestingly, the Blue Nile state, which has the majority of irrigated areas, has supported relatively low transmission across the state since the 1960s.

Table 2

State-wide percentage PfPR2–10 in 1961–64,39 2005,68,70,71 and 2008–0957 (positive and examined shown in parentheses)

States*1961–1964MIS 2005MIS 2009
Northern0.1 (3/3789)0.04 (2/4868)0 (0/904)
Khartoum0.001 (1/3138)0.1 (11/10595)0.1 (1/1621)
Kassala2.3 (138/5916)3.7 (22/595)1.0 (16/1546)
Darfur20.9 (563/2691)6.4 (41/638)1.6 (57/3623)
Kordofan17.7 (823/4654)14.1 (120/849)1.7 (38/2214)
Blue Nile3.1 (130/4185)2.8 (22/784)2.9 (116/3938)

States shown are those defined in 1961–64 and correspond to the contemporary states as follows: Northern (Northern and River Nile); Khartoum (Khartoum); Kassala (Kassala, Gedaref, and Red Sea); Darfur (West, North, and South Darfur); Kordofan (South and North Kordofan); and Blue Nile (Blue Nile and White Nile). Data from the 8 states surveyed in 2005 and the 15 states in 2009 were collapsed to match the boundaries of the 6 larger states in existence in the 1960s.

NMCP Sudan (2005). Gambiae project data report 2005. Sudan Federal Ministry of Health, unpublished data.

The 2005 data for Khartoum was assembled among all age groups by the KMFI.70PfPR2–10 = Plasmodium falciparum parasite rate; MIS = malaria indicator survey.

Figure 4.
Figure 4.

Historical expert opinion malaria risk map adapted from Nasr (1968).14 The current State boundaries are shown in light grey and the pre-1970 State boundaries are in black.

Citation: The American Society of Tropical Medicine and Hygiene 87, 6; 10.4269/ajtmh.2012.12-0390

Discussion

The use of empirical data is important to develop the cartography of malaria risk and is now relatively easy to accomplish at high spatial resolutions with the availability of mathematical and statistical tools and advances in computing speeds. To date the Republic of Sudan has relied on expert opinion maps14,68,69,89 developed from an informed set of climatic and location parameters that can now be quantified and modeled more accurately. Here, we have adapted the risk classifications of malaria that are currently defined in the Sudan national malaria strategic plan to suit the ambitions of the National Malaria Control Program (NMCP).57 This was achieved by combining the interpolated PfPR2–10 predictions with better definitions of aridity, urbanization, irrigation schemes, dams, camps for refugees and internally displaced persons settlements as shown in Figure 2. Table 1 shows a summary of the malaria strata matched with appropriate interventions defined through the use of WHO guidelines and other reference sources (see footnote to Table 1). The important differences between this matrix of malaria eco-epidemiology and control interventions and those currently used by the national program is the expanded extent and population at risk of the Desert Fringe, the more precise definition of the spatial extents of urban areas, irrigation schemes, refugee camps, and importantly, the disaggregation of the previous large area of “seasonal malaria” into hypo-, meso-, and hyper-endemic areas to support more targeted planning. The comparison of the historical and contemporary PfPR2–10 shows the possibility of a long-term epidemiological transition operating in areas of stable meso- to hyperendemic transmission which accelerates from 2005. This transition may partly be because of the significant scale-up of malaria interventions in Sudan in the last decade. Since 2004, almost 12 million long-lasting insecticidal nets (LLIN) were distributed in the country by the NMCP and partners (NMCP and partners, unpublished data) and by 2009 over 40% of households owned at least one LLIN.

Across Africa, linking the mapping of malaria risk to strategic plans is not as frequent as it should be, with only five countries using mapped malaria epidemiology to definitions of appropriate intervention within their national strategic plans or applications to the Global Fund.90 The reasons for this disconnect are not well understood but are likely to include the lack of any clear recommendations on the combinations of interventions that best suit a given eco-epidemiological risk strata. What is clear is that in areas with a historically low receptive endemicity that remain low today the universal distribution of ITN is unlikely to be a cost-efficient means of using limited malaria financial resources. Alternatives to control in these areas are likely to be active case detection and investigation and appropriate larval, source control methods at identifiable breeding sites.91 Conversely, high coverage of ITN in historically meso-/hyper-endemic areas is likely within a few years to result in low parasite prevalence92,93 and lead to substantial reductions in disease burden.94,95 The southern fringes of the southern states require rapidly scaled coverage of ITN, it would be reasonable to restrict ITN distribution to these areas only. Larval control, as currently promoted in the Khartoum and Gezira Free Malaria initiatives, is appropriate for the endemicity reductions in these areas, and could be expanded more aggressively to 16 of the 42 urban centers within the hypo-endemic belt (Figure 2). The feasibility of malaria elimination in the states of Northern, River Nile, and Red Sea with the dominant desert ecology and the large urban setting of Khartoum should be explored. Here, the wide-area modeling parasite prevalence survey data becomes less valuable. New techniques that combine improved clinical data, serological surveys, and mapping techniques that define hot-spots are necessary.9699

Malaria risk mapping was as important to control 50 years ago as it is today in the Republic of Sudan. Maps such as the one shown in Figure 2 developed using empirical national survey data are key to strategic planning of interventions for future malaria control and elimination in the Sudan. The study is a collaborative work with the Sudan NMCP and it anticipated this will facilitate the quick adoption of the malaria risk map for control planning. This, however, must be a dynamic exercise, which should be updated with new empirical and environmental data every few years. Special efforts must be invested in the data sparse areas along the southern border where predicted transmission is high but where, historically, insecurity has been a problem making the populations here even more vulnerable to the burden of malaria. Data in this area could be improved either by oversampling during the next MIS or by undertaking targeted surveys. Finally, the data required to develop empirical malaria risk maps are becoming increasingly available, but the geostatistical skills required to develop the spatial and temporal models are highly specialized and probably out of the reach of most national programs for the foreseeable future. The use of MCMC algorithms in which models take a long time to converge and require large computing resources is also an additional obstacle to routine development of geostatistical malaria maps. Recent developments of algorithms such as integrated nested Laplace approximations (INLA),100 however, offer opportunities for developing spatial-temporal models that converge rapidly without loss of predictive accuracy and can be run on an ordinary desktop.

ACKNOWLEDGMENTS

We thank Hoda Atta and Ghasem Zamani of WHO-EMRO for facilitating this collaborative work. We also acknowledge the contributions to the history of malaria control in the Sudan provided by Ahmed Elhassan and Jaffar Mirghani. We thank Samia Mirghani and Amal Nourien for help with assembly of the parasitological reports from Gezira and Khartoum, respectively. Finally, we are grateful to Punam Amratia, Caroline Kabaria, and Victor Alegana for help with archive searches outside of Sudan and geo-coding of survey data.

  • 1.

    World Health Organization, 1956. The World Health Organization and Malaria Eradication. Geneva: World Health Organization.

  • 2.

    Pampana E, 1969. Textbook of Malaria Eradication. Second edition. Oxford: Oxford University Press.

  • 3.

    Bagster-Wilson D, 1949. Malaria in British Somaliland. East Afr Med J 26: 288.

  • 4.

    Bechuanaland Protectorate, 1959. Annual Medical and Sanitary Report for the Protectorate for the Years 1958. Gaborone: Government Printers.

    • Search Google Scholar
    • Export Citation
  • 5.

    Butler RJ, 1959. Atlas of Kenya: A Comprehensive Series of New and Authenticated Maps Prepared from the National Survey and Other Governmental Sources with Gazetteer and Notes on Pronunciation and Spelling. Nairobi: the Survey of Kenya.

    • Search Google Scholar
    • Export Citation
  • 6.

    Cambournac FJ, Gandara AF, Pena AJ, Teixera WL, 1955. Subsidies for malacology survey in Angola [in Portuguese]. Annals of the Institute of Tropical Medicine (Lisbon) 12: 121152.

    • Search Google Scholar
    • Export Citation
  • 7.

    De Meillon B, 1951. Malaria survey of South-West Africa. Bull World Health Organ 4: 333417.

  • 8.

    Government of Tanganyika, 1956. Atlas of Tanganyika, East Africa. Dar es Salaam: Government Press.

  • 9.

    Government of Uganda, 1962. Atlas of Uganda. Kampala: Department of Lands and Survey.

  • 10.

    Guy Y, Gassabi R, 1967. The outlook for the eradication of malaria in Algeria [in French]. Archives of the Pasteur Institute of Algeria 45: 7288.

    • Search Google Scholar
    • Export Citation
  • 11.

    Hoeul G, Donadille F, 1953. Twenty years of malaria control in Morocco. Bulletin of the Institute of Hygiene Morocco 13: 351.

  • 12.

    Languillon J, 1957. Epidemiological map of malaria in Cameroon [in French]. Bulletin of the Society of Exotic Pathology Exotic and its Subsidiaries 50: 585601.

    • Search Google Scholar
    • Export Citation
  • 13.

    Massa F, 1936. Malaria Somala. G Med Mil 14: 643651.

  • 14.

    Nasr AH, 1968. Preparations for future malaria eradication programme in the Republic of the Sudan. University of Khartoum Faculty of Medicine. J Med Students Assoc 7: 178195.

    • Search Google Scholar
    • Export Citation
  • 15.

    World Health Organization–AFRO, 2012. Manual for Developing a National Malaria Strategic Plan. Brazzaville, Republic of Congo: WHO Regional Office for Africa.

    • Search Google Scholar
    • Export Citation
  • 16.

    Best N, Richardson S, Thomson A, 2005. A comparison of Bayesian spatial models for disease mapping. Stat Methods Med Res 14: 3559.

  • 17.

    Diggle PJ, Ribeiro PJ, 2007. Model-Based Geostatistics. New York: Springer.

  • 18.

    Patil AP, Gething PW, Piel FB, Hay SI, 2011. Bayesian geostatistics in health cartography: the perspective of malaria. Trends Parasitol 27: 246253.

    • Search Google Scholar
    • Export Citation
  • 19.

    Hay SI, Guerra CA, Gething PW, Patil AP, Tatem AJ, Noor AM, Kabaria CW, Manh BH, Elyazar IR, Brooker S, Smith DL, Moyeed RA, Snow RW, 2009. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 6: e1000048.

    • Search Google Scholar
    • Export Citation
  • 20.

    Craig MH, Sharp BL, Mabaso MLH, Kleinschmidt I, 2007. Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure. Int J Health Geogr 6: e44.

    • Search Google Scholar
    • Export Citation
  • 21.

    Gemperli A, Vounatsou P, Sogoba N, Smith T, 2006. Malaria mapping using transmission models: application to survey data from Mali. Am J Epidemiol 163: 289297.

    • Search Google Scholar
    • Export Citation
  • 22.

    Giardina F, Gosoniu L, Konate L, Diouf MB, Perry R, Gaye O, Faye O, Vounatsou P, 2012. Estimating the burden of malaria in Senegal: Bayesian zero-inflated binomial geostatistical modelling of the MIS 2008 data. PLoS ONE 7: e32625.

    • Search Google Scholar
    • Export Citation
  • 23.

    Gosoniu L, Veta AM, Vounatsou P, 2010. Bayesian geostatistical modeling of malaria indicator survey data in Angola. PLoS ONE 5: e9322.

  • 24.

    Gosoniu L, Msengwa A, Lengeler C, Vounatsou P, 2012. Spatially explicit burden estimates of malaria in Tanzania: Bayesian geostatistical modeling of the malaria indicator survey data. PLoS ONE 7: e23966.

    • Search Google Scholar
    • Export Citation
  • 25.

    Kazembe LN, Kleinschmidt I, Holtz TH, Sharp BL, 2006. Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data. Int J Health Geogr 5: e41.

    • Search Google Scholar
    • Export Citation
  • 26.

    Kleinschmidt I, Sharp BL, Clarke CPY, Curtis B, Fraser C, 2001. Use of generalized linear mixed models in the spatial analysis of small-area malaria incidence rates in KwaZulu Natal, South Africa. Am J Epidemiol 153: 12131222.

    • Search Google Scholar
    • Export Citation
  • 27.

    Noor AM, Clements ACA, Gething PW, Moloney G, Borle M, Shewshuk T, Hay SI, Snow RW, 2008. Spatial prediction of Plasmodium falciparum prevalence in Somalia. Malar J 7: 159.

    • Search Google Scholar
    • Export Citation
  • 28.

    Noor AM, Gething PW, Alegana VA, Patil AP, Hay SI, Muchiri E, Juma E, Snow RW, 2009. The risks of Plasmodium falciparum infection in Kenya in 2009. BMC Infect Dis 9: e180.

    • Search Google Scholar
    • Export Citation
  • 29.

    Raso G, Schur N, Utzinger J, Koudou BG, Tchicaya EM, Rohner F, N'Goran EK, Silué ED, Matthys B, Assi S, Tanner M, Vounatsou P, 2012. Mapping malaria risk among children in Cote d'Ivoire using Bayesian geo-statistical models. Malar J 11: 160.

    • Search Google Scholar
    • Export Citation
  • 30.

    Riedel N, Vounatsou P, Miller JM, Gosoniu L, Chizema-Kawesha E, Mukonka V, Steketee RW, 2010. Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS). Malar J 9: 37.

    • Search Google Scholar
    • Export Citation
  • 31.

    Taylor SM, Messina JP, Hand CC, Juliano JJ, Muwonga J, Tshefu AK, Atua B, Emch M, Meshnick SR, 2011. Molecular malaria epidemiology: mapping and burden estimates for the Democratic Republic of the Congo, 2007. PLoS ONE 6: e16420.

    • Search Google Scholar
    • Export Citation
  • 32.

    Snow RW, Alegana VA, Makomva K, Reich A, Uusiku P, Katokele S, Gething PW, Linard C, Tatem AJ, Moonen B, Noor AM, 2010. Estimating the distribution of malaria in Namibia in 2009: assembling the evidence and modelling risk. Ministry of Health & Social Services, Republic of Namibia & Malaria Atlas Project, May 2010.

    • Search Google Scholar
    • Export Citation
  • 33.

    Stensgaard A-S, Vounatsou P, Onapa AW, Simonsen PE, Pedersen EM, Rahbek C, Kristensen TK, 2011. Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co-endemicity. Malar J 10: 298.

    • Search Google Scholar
    • Export Citation
  • 34.

    Petrarca V, Nugud AD, Ahmed MA, Haridi AM, Di Deco MA, Coluzzi M, 2000. Cytogenetics of the Anopheles gambiae complex in Sudan, with special reference to An. arabiensis: relationships with East and West African populations. Mel & Vet Ento 14: 149164.

    • Search Google Scholar
    • Export Citation
  • 35.

    Lewis DJ, 1956. The anopheline mosquitoes of the Sudan. Bull Entomol Res 47: 475494.

  • 36.

    Zahar AR, 1985. Vector Bionomics in the Epidemiology and Control of Malaria. Part I.III. East Africa. Geneva: World Health Organization, WHO/VBC/85.3.

    • Search Google Scholar
    • Export Citation
  • 37.

    El Gaddal AA, 1985. The Blue Nile Health Project: a comprehensive approach to the prevention and control of water-associated diseases in irrigated schemes of the Sudan. J Trop Med Hyg 88: 4756.

    • Search Google Scholar
    • Export Citation
  • 38.

    Kempinska-Podhorodecka A, Knap O, Drozd A, Kaczmarczyk M, Parafiniuk M, Parczewski M, Ciechanowicz A, 2012. Analysis for genotyping Duffy blood group in inhabitants of Sudan, the Fourth Cataract of the Nile. Malar J 11: 115.

    • Search Google Scholar
    • Export Citation
  • 39.

    Wernsdorfer G, Wernsdorfer W, 1967. Malaria in the middle Nile basin and its bordering regions. Z Tropenmed Parasitol 18: 1744.

  • 40.

    Balfour A, 1913. A year's anti-malarial work at Khartoum. J Trop Med Hyg 14: 227232.

  • 41.

    Kushkush HA, 1968. Malaria in The Sudan: malaria, general background, history and its history in The Sudan. University of Khartoum Faculty of Medicine. J Med Students Assoc 7: 105110.

    • Search Google Scholar
    • Export Citation
  • 42.

    Salih H, Idris A, 1968. Malaria in The Sudan: malaria prevention and control. University of Khartoum Faculty of Medicine. J Med Students Assoc 7: 159168.

    • Search Google Scholar
    • Export Citation
  • 43.

    World Health Organization-Sudan, 1956. Information on the Malaria Control Programme in the Sudan. WHO EMRO inter-regional meeting, April 1956. World Health Organization, Athens Meeting, WHO/MAL/163-14.

    • Search Google Scholar
    • Export Citation
  • 44.

    El Gaddal AA, 1968. Malaria in The Sudan: malaria pilot project Sudan 6 (1956–1960). University of Khartoum Faculty of Medicine. J Mel Students Assoc 7: 197200.

    • Search Google Scholar
    • Export Citation
  • 45.

    Henderson LH, 1934. Prophylaxis of malaria in The Sudan with special reference to plasmoquine. Trans R Soc Trop Med Hyg 28: 157164.

  • 46.

    Sudan National Malaria Administration, 1998. Blue Nile Health Project, Annual Report. Ministry of Health Sudan.

  • 47.

    Mirghani ES, Nour BY, Bushra SM, Hassan El I, Snow RW, Noor AM, 2010. The spatial-temporal clustering of Plasmodium falciparum infection over eleven years in Gezira State, The Sudan. Malar J 9: e172.

    • Search Google Scholar
    • Export Citation
  • 48.

    Federal Ministry of Health, 2004. Documentation of Khartoum and Gezira Malaria Initiatives. Khartoum: Federal Ministry of Health, 133.

    • Search Google Scholar
    • Export Citation
  • 49.

    Khalifa SM, Mustafan IO, Wais M, Malik EM, 2008. Malaria control in an urban area; a successful story from Khartoum, 1995–2004. La Revue de Santé de la Méditerranée Orientale 14: 206215.

    • Search Google Scholar
    • Export Citation
  • 50.

    Malik EM, Ahmed ES, Elkhalifa SM, Hussein MA, Suleiman AMN, 2003. Stratification of Khartoum urban area by the risk of malaria transmission. East Mediterr Health J 9: 559569.

    • Search Google Scholar
    • Export Citation
  • 51.

    Shousha AT, 1948. The eradication of A. gambiae from Upper Egypt, 1942–1945. Bull World Health Organ 1: 309348.

  • 52.

    Farid MA, 1984. A mission to the Gambiae Project area in Egypt and Sudan. 13 February to 15 March 1984. World Health Organization, WHO-EM/MAL/204.

    • Search Google Scholar
    • Export Citation
  • 53.

    Mashaal HAH, Dukeen MYH, Zarroug IMA, 1988. Assessment of malaria in Sennar sugar project: 7–18 February 1987. Report by Independent In-depth Review Team to WHO-EMRO.

    • Search Google Scholar
    • Export Citation
  • 54.

    El Gaddal AA, 1986. Malaria in the Sudan. Proceedings of the Conference on Malaria in Africa. Practical considerations on malaria vaccine and clinical trials. Washington, DC, December 1–4, 156159.

    • Search Google Scholar
    • Export Citation
  • 55.

    Malik EM, Atta HY, Weis M, Land A, Puta C, Lettenmaier C, Bell A, 2004. Sudan Roll Back Malaria consultative mission essential actions to support the attainment of the Abuja targets. 16–20 November 2003. SUDAN RBM Country Consultative Mission Final Report.

    • Search Google Scholar
    • Export Citation
  • 56.

    Federal Ministry of Health - National Malaria Control Programme, 2006. National Malaria Strategic Plan 2007–2012. Khartoum.

  • 57.

    Federal Ministry of Health - National Malaria Control Programme, 2010. Five Years Strategic Plan for the National Malaria Control Programme Sudan, 2011–2015. Khartoum.

    • Search Google Scholar
    • Export Citation
  • 58.

    Shililu JI, Grueber WB, Mbogo CM, Githure JI, Riddiford LM, Beier JC, 2004. Development and survival of Anopheles gambiae eggs in drying soil: influence of the rate of drying, egg age, and soil type. J Am Mosq Control Assoc 20: 243247.

    • Search Google Scholar
    • Export Citation
  • 59.

    Gray EM, Bradley TJ, 2005. Physiology of desiccation resistance in Anopheles gambiae and Anopheles arabiensis. Am J Trop Med Hyg 73: 553559.

    • Search Google Scholar
    • Export Citation
  • 60.

    MODIS-EVI data archives, 2005. Available at: ftp://n4ftl01u.ecs.nasa.gov/SAN/MOST/MOD10A2.005/. Accessed 15 June, 2011.

  • 61.

    Guerra CA, Snow RW, Hay SI, 2006. Defining the global limits of malaria transmission in 2005. Adv Parasitol 62: 157179.

  • 62.

    Guerra CA, Gikandi PW, Tatem AJ, Noor AM, Smith DL, Hay SI, Snow RW, 2008. The limits and intensity of Plasmodium falciparum: implications for malaria control and elimination worldwide. PLoS Med 5: e38.

    • Search Google Scholar
    • Export Citation
  • 63.

    Balk D, Pozzi F, Yetman G, Deichmann U, Nelson A, 2004. The Distribution of People and the Dimension of Place: Methodologies to Improve Global Population Estimates in Urban and Rural Areas. New York: CIESIN Columbia University.

    • Search Google Scholar
    • Export Citation
  • 64.

    Balk D, Pullum T, Storeyguard A, Greenwell F, Neuman M, 2004. A spatial analysis of childhood mortality in West Africa. Popul Space Place 10: 175216.

    • Search Google Scholar
    • Export Citation
  • 65.

    Global Lakes and Wetlands Database, 2004. Available at: https://secure.worldwildlife.org/science/data/item1877.html. Accessed June 10, 2011.

    • Search Google Scholar
    • Export Citation
  • 66.

    Wallach B, 2004. Irrigation in Sudan since Independence. Geogr Rev 74: 127144.

  • 67.

    United Nations High Commission for Refugees, 2012. Sudan. Available at: http://www.unhcr.org/pages/49e483b76.html. Accessed 20 March, 2012.

    • Search Google Scholar
    • Export Citation
  • 68.

    Federal Ministry of Health - National Malaria Control Programme, 2005. Malaria Prevalence and Coverage Indicators Survey Sudan – October 2005. Final Report, December 2005.

    • Search Google Scholar
    • Export Citation
  • 69.

    Federal Ministry of Health - National Malaria Control Programme, 2010. Malaria Indicator Survey 2009 in the Northern States of the Sudan. Final Report, March 2010.

    • Search Google Scholar
    • Export Citation
  • 70.

    Nourien AB, Abass MA, Abdel Nugud AD, El Hassan I, Snow RW, Noor AM, 2011. Identifying residual foci of Plasmodium falciparum infections for malaria elimination: the urban context of Khartoum, Sudan. PLoS ONE 6: e16948.

    • Search Google Scholar
    • Export Citation
  • 71.

    Federal Ministry of Health, Sudan, 2009. Malaria Parasitology Survey Reports of Gambiae project, 1999–2009. Sudan: Federal Ministry of Health.

    • Search Google Scholar
    • Export Citation
  • 72.

    Smith DL, Guerra CA, Snow RW, Hay SI, 2007. Standardizing estimates of malaria prevalence. Malar J 6: e131.

  • 73.

    Malaria Atlas Project, 2010. P. falciparum Cartographic code. Available at: https://github.com/malaria-atlas-project/mbgw-clean. Accessed March 10, 2011.

    • Search Google Scholar
    • Export Citation
  • 74.

    Noor AM, Alegana VA, Moloney G, Borle M, Ahmed F, Yousef F, Amran J, Snow RW, 2012. Mapping the receptivity of malaria risk to plan the future of control in Somalia. BMJ Open 2: e001160.

    • Search Google Scholar
    • Export Citation
  • 75.

    Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A, 2005. Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25: 19651978.

    • Search Google Scholar
    • Export Citation
  • 76.

    Gething PW, Van Boeckel T, Smith DL, Guerra CA, Patil AP, Hay SI, 2011. Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax. Parasit Vector 4: 92.

    • Search Google Scholar
    • Export Citation
  • 77.

    Miller A, 2002. Subset Selection in Regression. Boca Raton, FL: Chapman & Hall.

  • 78.

    Lumley T, 2010. Leaps: regression subset selection (R package) version 2.7.

  • 79.

    Schwarz G, 1978. Estimating dimensions of a model. Ann Stat 6: 461464.

  • 80.

    Linard C, Gilbert M, Snow RW, Noor AM, Tatem AJ, 2012. Population distribution, settlement patterns and accessibility across Africa in 2010. PLoS ONE 7: e31743.

    • Search Google Scholar
    • Export Citation
  • 81.

    The AfriPop Project, 2009. Available at: http://www.clas.ufl.edu/users/atatem/index_files/Details.htm. Accessed March 12, 2012.

  • 82.

    UNHCR, 2012. Country Operations Profile - Sudan. Available at: http://www.unhcr.org/pages/49e483b76.html. Accessed May 12, 2010.

  • 83.

    WHO, 2005. Malaria Control in Complex Emergencies: An Interagency Field Handbook. http://whqlibdoc.who.int/publications/2005/924159389X_eng.pdf. Accessed May 3, 2012.

    • Search Google Scholar
    • Export Citation
  • 84.

    Najera JA, 1996. Malaria Control among Refugees and Displaced Populations. World Health Organization, Division of Control of Tropical Diseases, Malaria Unit.

    • Search Google Scholar
    • Export Citation
  • 85.

    UNHCR, 2008. Strategic Plan for Malaria Control 2008–2012. Available at: http://www.unhcr.org/488597e02.html. Accessed May 14, 2012.

  • 86.

    De Castro MC, Yamagata Y, Mtasiwa D, Tanner M, Utzinger J, Keiser J, Singer BH, 2004. Integrated urban malaria control: a case study in Dar Es Salaam, Tanzania. Am J Trop Med Hyg 71: 103117.

    • Search Google Scholar
    • Export Citation
  • 87.

    Fillinger U, Kannady K, William G, Vanek MJ, Dongus S, Nyika D, Geissbühler Y, Chaki PP, Govella NJ, Mathenge EM, Singer BH, Mshinda H, Lindsay SW, Tanner M, Mtasiwa D, Castro MC, Killeen GF, 2008. A tool box for operational mosquito larval control: preliminary results and early lessons from the Urban Malaria Control Programme in Dar es Salaam, Tanzania. Malar J 7: 20.

    • Search Google Scholar
    • Export Citation
  • 88.

    Sudan Medical Service, 1931. Report on Medical and Health Work in the Sudan for the Year 1930. (Sudan) Limited Khartoum: McCorquodale & Co.

    • Search Google Scholar
    • Export Citation
  • 89.

    Elgaddal 1991. Malaria in the Sudan. Malaria and Development in Africa: a Cross-Sectional Approach. A report by the American Association for the Advancement of Science.

    • Search Google Scholar
    • Export Citation
  • 90.

    Omumbo JA, Noor AM, Fall IS, Snow RW, 2012. How Well is Malaria Risk Cartography Used to Design and Finance Malaria Control in Africa? Report prepared for DFID-UK and partners, June 2012.

    • Search Google Scholar
    • Export Citation
  • 91.

    WHO 2008. Global Malaria Control and Elimination: Report of a Technical Review. Available at: http://www.who.int/malaria/publications/atoz/9789241596756/en/index.html. Accessed June 10, 2012.

    • Search Google Scholar
    • Export Citation
  • 92.

    Smith DL, Noor AM, Hay SI, Snow RW, 2009. Predicting changing malaria risk following expanded insecticide treated net coverage in Africa. Trends Parasitol 25: 511516.

    • Search Google Scholar
    • Export Citation
  • 93.

    Griffin JT, Hollingsworth TD, Okell LC, Churcher TS, White M, Hinsley W, Bousema T, Drakeley CJ, Ferguson NM, Basáñez MG, Ghani AC, 2010. Reducing Plasmodium falciparum malaria transmission in Africa: a model-based evaluation of intervention strategies. PLoS Med 7: e1000324.

    • Search Google Scholar
    • Export Citation
  • 94.

    Snow RW, Marsh K, 2002. The consequences of reducing Plasmodium falciparum transmission in Africa. Adv Parasitol 52: 235264.

  • 95.

    Snow RW, Marsh K, 2010. Malaria in Africa: progress and prospects in the decade since the Abuja Declaration. Lancet 376: 137139.

  • 96.

    Drakeley CJ, Corran PH, Coleman PG, Tongren JE, McDonald SL, Carneiro I, Malima R, Lusingu J, Manjurano A, Nkya WM, Lemnge MM, Cox J, Reyburn H, Riley EM, 2005. Estimating medium- and long-term trends in malaria transmission by using serological markers of malaria exposure. PNAS USA 102: 51085113.

    • Search Google Scholar
    • Export Citation
  • 97.

    Noor AM, Mohamed MB, Mugyenyi C, Osman MA, Guessod HH, Kabaria CW, Nyonda M, Cook J, Drakelely CJ, Mackinnon MJ, Snow RW, 2011. Establishing the extent of malaria transmission and challenges facing pre-elimination in the Republic of Djibouti. BMC Infect Dis 11: 121.

    • Search Google Scholar
    • Export Citation
  • 98.

    Bejon P, Liljander A, Noor AM, Wambua J, Ogada E, Olotu A, Osier F, Hay S, Färnert A, Marsh K, 2010. Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. PLoS Med 7: e1000304.

    • Search Google Scholar
    • Export Citation
  • 99.

    Bousema T, Griffin JT, Sauerwein RW, Smith DL, Churcher TS, Taken W, Ghani A, Drakeley C, Gosling R, 2012. Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med 9: e1001165.

    • Search Google Scholar
    • Export Citation
  • 100.

    Rue H, Martino S, 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J R Stat Soc, B 71: 319392.

    • Search Google Scholar
    • Export Citation
  • 101.

    Roll Back Malaria, 2008. The Global Malaria Action Plan. Roll Back Malaria partnership. Geneva: World Health Organization.

  • 102.

    Cohen JM, Moonen B, Snow RW, Smith DL, 2010. How absolute is zero? An evaluation of historical and current definitions of malaria elimination. Malar J 9: 213.

    • Search Google Scholar
    • Export Citation

Author Notes

* Address correspondence to Abdisalan M. Noor, Malaria Public Health Cluster, Centre for Geographic Medicine, KEMRI – University of Oxford – Wellcome Trust Research Programme, Kenyatta National Hospital Grounds (behind NASCOP), P.O. Box 43640-00100, Nairobi, Kenya. E-mail: anoor@nairobi.kemriwellcome.org

Financial support: AMN is supported by the Wellcome Trust as an Intermediate Research Fellow (#095127). RWS is supported by the Wellcome Trust as Principal Research Fellow (#079080) and both acknowledge programmatic support provided by the Wellcome Trust Major Overseas Programme grant to the KEMRI/Wellcome Trust Research Programme (#092654). All authors are grateful for the support provided by the Federal Ministry of Health.

Authors' addresses: Abdisalan M. Noor, Malaria Public Health Theme, Centre for Geographic Medicine Research, Coast, Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya, E-mail: anoor@nairobi.kemri-wellcome.org. Khalid A. ElMardi and Tarig M. Abdelgader, Federal Ministry of Health, National Malaria Control Programme, Khartoum, Khartoum, Sudan, E-mails: khalidmrd9@hotmail.com and tarigmohamad@hotmail.com. Anand P. Patil, Sense Inc., Technical, Detroit, MI, E-mail: anand.prabhakar.patil@gmail.com. Ahmed A. A. Amine, Sahar Bakhiet, and Maowia M. Mukhtar, University of Khartoum, Institute of Endemic Diseases, Khartoum, Khartoum, Sudan, E-mails: nazooone@hotmail.com, saharbakhiet@iend.org, and mmukhtar@iend.org. Robert W. Snow, KEMRI-University of Oxford-Wellcome Trust Research Programme, Malaria Public Health Theme, Nairobi, Nairobi, Kenya, E-mail: rsnow@nairobi.kemri-wellcome.org.

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