The online version of this article (doi:10.1186/1475-2875-11-165) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
AM and MCW designed the study, processed the satellite data, analysed the data, and drafted the manuscript; GMH participated in satellite data processing and drafting the manuscript. GS also participated in drafting the manuscript. PS developed the historical malaria surveillance database. The authors all read and approved the manuscript.
Malaria is one of the leading public health problems in most of sub-Saharan Africa, particularly in Ethiopia. Almost all demographic groups are at risk of malaria because of seasonal and unstable transmission of the disease. Therefore, there is a need to develop malaria early-warning systems to enhance public health decision making for control and prevention of malaria epidemics. Data from orbiting earth-observing sensors can monitor environmental risk factors that trigger malaria epidemics. Remotely sensed environmental indicators were used to examine the influences of climatic and environmental variability on temporal patterns of malaria cases in the Amhara region of Ethiopia.
In this study seasonal autoregressive integrated moving average (SARIMA) models were used to quantify the relationship between malaria cases and remotely sensed environmental variables, including rainfall, land-surface temperature (LST), vegetation indices (NDVI and EVI), and actual evapotranspiration (ETa) with lags ranging from one to three months. Predictions from the best model with environmental variables were compared to the actual observations from the last 12 months of the time series.
Malaria cases exhibited positive associations with LST at a lag of one month and positive associations with indicators of moisture (rainfall, EVI and ETa) at lags from one to three months. SARIMA models that included these environmental covariates had better fits and more accurate predictions, as evidenced by lower AIC and RMSE values, than models without environmental covariates.
Malaria risk indicators such as satellite-based rainfall estimates, LST, EVI, and ETa exhibited significant lagged associations with malaria cases in the Amhara region and improved model fit and prediction accuracy. These variables can be monitored frequently and extensively across large geographic areas using data from earth-observing sensors to support public health decisions.
Federal Democratic Republic of Ethiopia, Ministry of Health: Ethiopia National Malaria Indicator Survey 2007. 2008, Addis Ababa
Graves PM, Richards FO, Ngondi J, Emerson PM, Shargie EB, Endeshaw T, Ceccato P, Ejigsemahu Y, Mosher AW, Hailemariam A, Zerihun M, Teferi T, Ayele B, Mesele A, Yohannes G, Tilahun A, Gebre T: Individual, household and environmental risk factors for malaria infection in Amhara, Oromia and SNNP regions of Ethiopia. Trans R Soc Trop Med Hyg. 2009, 103: 1211-1220. 10.1016/j.trstmh.2008.11.016. CrossRefPubMed
WHO: Malaria Early Warning Systems, Concepts, Indicators and patterns. A Framework for Field Research in Africa. 2001, WHO, Geneva
Abeku TA, De Vlas SJ, Borsboom GJJM, Tadege A, Gebreyesus Y, Gebreyohannes H, Alamirew D, Seifu A, Nagelkerke NJD, Habbema JDF: Effects of meteorological factors on epidemic malaria in Ethiopia: a statistical modelling approach based on theoretical reasoning. Parasitology. 2004, 128: 585-593. 10.1017/S0031182004005013. CrossRefPubMed
Abeku TA, de Vlas SJ, Borsboom G, Teklehaimanot A, Kebede A, Olana D, van Oortmarssen GJ, Habbema JDF: Forecasting malaria incidence from historical morbidity patterns in epidemic-prone areas of Ethiopia: a simple seasonal adjustment method performs best. Trop Med Int Health. 2002, 7: 851-857. 10.1046/j.1365-3156.2002.00924.x. CrossRefPubMed
Senay G, Verdin J: Developing a malaria early warning system for Ethiopia. Proceedings of theTwenty-Fifth Annual ESRI International User Conference: 25–29. 2005, San Diego, California, July
Dinku T, Ceccato P, Grover-Kopec E, Lemma M, Connor SJ, Ropelewski CF: Validation of satellite rainfall products over East Africa's complex topography. Int J Remote Sens. 2007, 28: 1503-1526. 10.1080/01431160600954688. CrossRef
Tatem AJ, Scott JG, Hay SI: Terra and Aqua: new data for epidemiology and public health. Int J Appl Earth Obs. 2004, 6: 33-46. 10.1016/j.jag.2004.07.001. CrossRef
Pinzon JE, Wilson JM, Tucker CJ, Arthur R, Jahrling PB, Formenty P: Trigger events: Enviroclimatic coupling of ebola hemorrhagic fever outbreaks. AmJTrop Med Hyg. 2004, 71: 664-674.
Ceccato P, Connor S, Jeanne I, Thomson M: Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk. Parassitologia. 2005, 47: 81-96. PubMed
Ceccato P, Ghebremeskel T, Jaiteh M, Graves PM, Levy M, Ghebreselassie S, Ogbamariam A, Barnston AG, Bell M, del Corral J, Connor SJ, Fesseha I, Brantly EP, Thomson MC: Malaria stratification, climate, and epidemic early warning in Eritrea. AmJTrop Med Hyg. 2007, 77: 61-68.
Mu Q, Heinsch FA, Zhao M, Running SW: Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens Environ. 2007, 111: 519-536. 10.1016/j.rse.2007.04.015. CrossRef
Burnham KP, Anderson DR: Model Selection and Multimodel Inference: A Practical Information Theoretic Approach. 2002, Springer, New York
Mildrexler DJ, Zhao MS, Running SW: A global comparison between station air temperatures and MODIS land surface temperatures reveals the cooling role of forests. J Geophys Res. 2011, 116: 0148-0227.
Vancutsem C, Ceccato P, Dinku T, Connor SJ: Evaluation of MODIS land surface temperature data to estimate air temperature in different ecosystems over Africa. Remote Sens Environ. 2010, 114: 449-465. 10.1016/j.rse.2009.10.002. CrossRef
Patz JA, Strzepek K, Lele S, Hedden M, Greene S, Noden B, Hay SI, Kalkstein L, Beier JC: Predicting key malaria transmission factors, biting and entomological inoculation rates, using modelled soil moisture in Kenya. Trop Med Int Health. 1998, 3: 818-827. 10.1046/j.1365-3156.1998.00309.x. CrossRefPubMed
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG: Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 2002, 83: 195-213. 10.1016/S0034-4257(02)00096-2. CrossRef
Rocha AV, Shaver GR: Advantages of a two band EVI calculated from solar and photosynthetically active radiation fluxes. Agr Forest Meteorol. 2009, 149: 1560-1563. 10.1016/j.agrformet.2009.03.016. CrossRef
Viña A, Henebry GM, Gitelson AA: Satellite monitoring of vegetation dynamics: Sensitivity enhancement by the Wide Dynamic Range Vegetation Index. Geophys Res Lett. 2004, 31: L04503- CrossRef
Teklehaimanot HD, Schwartz J, Teklehaimanot A, Lipsitch M: Weather-based prediction of Plasmodium falciparum malaria in epidemic-prone regions of Ethiopia II. Weather-based prediction systems perform comparably to early detection systems in identifying times for interventions. Malar J. 2004, 3: 44-10.1186/1475-2875-3-44. PubMedCentralCrossRefPubMed
- Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia
Geoffrey M Henebry
Michael C Wimberly
- BioMed Central
Neu im Fachgebiet Innere Medizin
Meistgelesene Bücher aus der Inneren Medizin
Mail Icon II