Volume 77, Issue 6_Suppl
  • ISSN: 0002-9637
  • E-ISSN: 1476-1645


Eritrea has a successful malaria control program, but it is still susceptible to devastating malaria epidemics. Monthly data on clinical malaria cases from 242 health facilities in 58 (districts) of Eritrea from 1996 to 2003 were used in a novel stratification process using principal component analysis and nonhierarchical clustering to define five areas with distinct malaria intensity and seasonality patterns, to guide future interventions and development of an epidemic early warning system. Relationships between monthly clinical malaria incidence by and monthly climate data from several sources, and with seasonal climate forecasts, were investigated. Remotely sensed climate data were averaged over the same geographic administrative units as the malaria cases. Although correlation was good between malaria anomalies and actual rainfall from ground stations (lagged by 2 months), the stations did not have sufficiently even coverage to be widely useful. Satellite derived rainfall from the Climate Prediction Center Merged Analysis of Precipitation was correlated with malaria incidence anomalies, with a lead time of 2–3 months. NDVI anomalies were highly correlated with malaria incidence anomalies, particularly in the semi-arid north of the country and along the northern Red Sea coast, which is a highly epidemic-prone area. Eritrea has 2 distinct rainy seasons in different parts of the country. The seasonal forecasting skill from Global Circulation Models for the June/July/August season was low except for the Eastern border. For the coastal October/November/December season, forecasting skill was good only during the 1997–1998 El Niño event. For epidemic control, shorter-range warning based on remotely sensed rainfall estimates and an enhanced epidemic early-detection system based on data derived for this study are needed.


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  1. Sintasath DM, Ghebremeskel T, Lynch M, Kleinau E, Bretas G, Shililu J, Brantly E, Graves PM, Beier JC, 2005. Malaria prevalence and associated risk factors in Eritrea. Am J Trop Med Hyg 72 : 682–687. [Google Scholar]
  2. Nyarango PM, Gebremeskel T, Mebrahtu G, Mufunda J, Abdulmumini U, Ogbamariam A, Kosia A, Gebremichael A, Gunawardena D, Ghebrat Y, and Okbaldet Y, 2006. A steep decline in malaria morbidity and mortality trends in Eritrea between 2000 and 2004: the effect of a combination of control methods. Malaria J 5 : 33. [Google Scholar]
  3. Macintyre K, Keating J, Okbaldt YB, Zerom M, Sosler S, Ghebremeskel T, Eisele TP, 2006. Rolling out insecticide treated nets in Eritrea: examining the determinants of possession and use in malarious zones during the rainy season. Trop Med Int Health 11 : 824–833. [Google Scholar]
  4. Shililu JI, Tewolde GM, Brantly E, Githure JI, Mbogo CM, Beier JC, Fusco R, Novak JL, 2003. Efficacy of Bacillus thuringiensis israelensis, Bacillus sphaericus and temephos for managing Anopheles larvae in Eritrea. J Am Mosq Cont Assoc 19 : 251–258. [Google Scholar]
  5. WHO, 2001. Malaria Early Warning Systems, Concepts, Indicators and Partners. A Framework for Field Research in Africa. Geneva: WHO.
  6. WHO, 2004. Field Guide for Malaria Epidemic Assessment and Reporting. Draft for Field Testing. Geneva: World Health Organization. WHO/HTM/MAL/2004.1097.
  7. Craig MH, Snow RW, le Sueur D, 1999. A climate-based distribution model of malaria transmission in sub-Saharan Africa. Parasitol Today 15 : 105–111. [Google Scholar]
  8. Bretas G, 2001. Malaria Risk Stratification in Eritrea. Report to the Environmental Health Project, Washington, DC: USAID.
  9. Thomson M, Graves PM, Barnston AG, Bell M, Ceccato P, Connor S, del Corral J, Giannini A, Obsomer V, Wolde-Georgis T, Jaiteh M, Levy M, Lukang L, 2005. Towards a Malaria Early Warning System for Eritrea. Final Report to Environmental Health Project, Washington, DC: USAID.
  10. Dinku T, Ceccato P, Grover-Kopec E, Lemma M, Connor SJ, Ropelewski CF, 2007. Validation and inter-comparison of satellite rainfall products over East Africa’s complex topography. Int J Remote Sens 28 : 1503–1526. [Google Scholar]
  11. Davenport ML, Nicholson SE, 1993. On the relation between rainfall and the normalized difference vegetation index for diverse vegetation types in east Africa. Int J Remote Sens 14 : 2369–2389. [Google Scholar]
  12. Nicholson SE, Farrar TJ, 1994. The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. 1. NDVI response to rainfall. Remote Sens Environ 50 : 107–120. [Google Scholar]
  13. Anyamba A, Eastman JR, 1996. Interannual variability of NDVI over Africa and its relation to El Niño–Southern Oscillation. Int J Remote Sens 17 : 2533–2548. [Google Scholar]
  14. Goddard L, Mason SJ, Zebiak SE, Ropelewski CF, Basher R, Cane MA, 2001. Current approaches to seasonal-to-inter-annual climate predictions. Int J Climatol 21 : 1111–1152. [Google Scholar]
  15. Seleshi Y, Demaree GR, 1995. Rainfall variability in the Ethiopian and Eritrean highlands and its links with the Southern Oscillation Index. J Biogeogr 22 : 945–952. [Google Scholar]
  16. Kovats RS, Bouma MJ, Hajat S, Worrall E, Haines A, 2003. El Niño and health. Lancet 362 : 1481–1489. [Google Scholar]
  17. Thomson MC, Connor SJ, Phindela T, Mason SJ, 2005. Rainfall and sea-surface temperature monitoring for malaria early warning in Botswana. Am J Trop Med Hyg 73 : 214–221. [Google Scholar]
  18. Thomson MC, Doblas-Reyes FJ, Mason SJ, Hagedorn R, Connor SJ, Phindela T, Morse AP, Palmer TN, 2006. Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439 : 576–579. [Google Scholar]
  19. DaSilva J, Garanganga B, Teveredzi V, Marx S, Mason SJ, Connor SJ, 2004. Improving epidemic malaria planning, preparedness and response in southern Africa. Malaria J 3 : 37. [Google Scholar]
  20. Allard R, 1998. Use of time-series analysis in infectious disease surveillance. Bull World Health Organ 76 : 327–333. [Google Scholar]
  21. Chaulagai CN, Moyo CM, Koot J, Moyo HB, Sambakunsi TC, Khunga FM, Naphini PD, 2005. Design and implementation of a health management information system in Malawi: issues, innovations and results. Health Policy Plan 20 : 375–384. [Google Scholar]
  22. Chilundo B, Sundby J, Aanestad M, 2004. Analysing the quality of routine malaria data in Mozambique. Malaria J 3 : 3. [Google Scholar]
  23. Chandramohan D, Jaffar S, Greenwood B, 2002. Use of clinical algorithms for diagnosing malaria. Trop Med Int Health 7 : 45–52. [Google Scholar]
  24. Mabaso MLH, Craig M, Vounatsou P, Smith T, 2005. Towards empirical description of malaria seasonality in southern Africa: the example of Zimbabwe. Trop Med Int Health 10 : 909–918. [Google Scholar]
  25. Gething PM, Noor AM, Gikandi PW, Ogara EAA, Hay SI, Nixon MS, Snow RW, Atkinson PM, 2006. Improving imperfect data from health management information systems in Africa using space–time geostatistics. PLoS Med 3 : 6. [Google Scholar]
  26. Raghunathan TE, Lepkowski JE, Solenberger PW, Van Hoewyk JH, 2001. A multivariate technique for multiply imputing missing values using a sequence of regression models. Surv Methodol 27 : 85. [Google Scholar]
  27. Rubin DB, 1996. Multiple imputation after 18+ years. J Am Stat Assoc 91 : 473–489. [Google Scholar]
  28. Schafer JL, 1997. Analysis of Incomplete Multivariate Data. London: Chapman & Hall.
  29. Griguolo S, Mazzanti M, 1998. ADDATI: Un Package per l’Analisi Esplorativa dei Dati. V.4.0. Guida all’Uso. Padova: Editrice Libreria Progretto, 202 pp.
  30. Diday E, 1971. Une nouvelle méthode en classification automatique et reconnaissance des formes la méthode des nuées dynamiques. Rev Statist Appl 19 : 19–33. [Google Scholar]
  31. Jollife AT, 1986. Principal Component Analysis. New York: Springer.
  32. Di Gregorio A, Mansen LJM, 2000. Land Cover Classification System, Classification Concepts and User Manual. Rome: Food and Agriculture Organization of the United Nations, 179 pp.
  33. Booman M, Durrheim DN, La Grange K, Martin C, Mabuza AM, Zitha A, Mbokazi FM, Fraser C, Sharp BL, 2000. Using a geographical information system to plan a malaria control program in South Africa. Bull World Health Organ 78 : 1438–1444. [Google Scholar]
  34. Wickramasinghe AR, Gunawardena DM, Mahawithanage ST, 2002. Use of routinely collected past surveillance data in identifying and mapping high risk areas in a malaria endemic area of Sri Lanka. SE Asian J Trop Med Publ Health 33 : 678–684. [Google Scholar]
  35. Goddard L, Graham NE, 1999. The importance of the Indian Ocean for simulating precipitation anomalies over eastern and southern Africa. J Geophys Res 104 : 19099–19116. [Google Scholar]
  36. Korecha D, Barnston AG. 2007. Predictability of June to September rainfall in Ethiopia. Monthly Weather Rev 135 : 628–650. [Google Scholar]
  37. New M, Hulme M, Jones P, 2000. Representing twentieth-century space–time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate. J Clim 13 : 2217–2238. [Google Scholar]

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  • Received : 21 Aug 2006
  • Accepted : 16 Apr 2007

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