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. 2017 Jun 1;7(1):2589.
doi: 10.1038/s41598-017-02560-z.

Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya

Maquins Odhiambo Sewe  1   2 , Yesim Tozan  3   4 , Clas Ahlm  5 , Joacim Rocklöv  6   7
Affiliations

Using remote sensing environmental data to forecast malaria incidence at a rural district hospital in Western Kenya

Maquins Odhiambo Sewe et al. Sci Rep. .

Erratum in

Abstract

Malaria surveillance data provide opportunity to develop forecasting models. Seasonal variability in environmental factors correlate with malaria transmission, thus the identification of transmission patterns is useful in developing prediction models. However, with changing seasonal transmission patterns, either due to interventions or shifting weather seasons, traditional modelling approaches may not yield adequate predictive skill. Two statistical models,a general additive model (GAM) and GAMBOOST model with boosted regression were contrasted by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to three months. Monthly admission data for children under five years with confirmed malaria at the Siaya district hospital in Western Kenya for the period 2003 to 2013 were used together with satellite derived data on rainfall, average temperature and evapotranspiration(ET). There was a total of 8,476 confirmed malaria admissions. The peak of malaria season changed and malaria admissions reduced overtime. The GAMBOOST model at 1-month lead time had the highest predictive skill during both the training and test periods and thus can be utilized in a malaria early warning system.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Monthly average of pediatric malaria admissions (a), mean LST (b), ET (c) and precipitation (d) in Karemo division, Siaya county, Western Kenya, 2003–2013.
Figure 2
Figure 2
Monthly distribution of pediatric malaria admissions by year at Siaya district hospital in Karemo division, Siaya county, Western Kenya, 2003–2013.
Figure 3
Figure 3
Observed and predicted pediatric malaria admissions at Siaya district hospital, Western Kenya by prediction lead time for the period 2003–2013 from the GAMBOOST model. (a) The 1-month, (b) the 2-month and (c) the 3-month prediction lead times respectively. The black line displays observed malaria admissions, the grey line predicted values during the training period 2003–2012, and the red line the 2013 forecasted values. The dotted red line marks the beginning of the test period.
Figure 4
Figure 4
Observed and predicted pediatric malaria admissions at Siaya district hospital, Western Kenya by prediction lead time for the period 2003–2013 from the GAM model. (a) The 1-month, (b) the 2-month and (c) the 3-month prediction lead times respectively. The black line displays observed malaria admissions, the grey line predicted values during the training period 2003–2012, and the red line the 2013 forecasted values. The dotted red line marks the beginning of the test period.
See this image and copyright information in PMC

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