Review
doi: 10.1038/415710a.
Satellite imagery in the study and forecast of malaria
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- PMID: 11832960
- PMCID: PMC3160466
- DOI: 10.1038/415710a
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Review
Satellite imagery in the study and forecast of malaria
David J Rogers et al.
Nature.
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Abstract
More than 30 years ago, human beings looked back from the Moon to see the magnificent spectacle of Earth-rise. The technology that put us into space has since been used to assess the damage we are doing to our natural environment and is now being harnessed to monitor and predict diseases through space and time. Satellite sensor data promise the development of early-warning systems for diseases such as malaria, which kills between 1 and 2 million people each year.
Figures
Hypothetical relationship between the challenge to a host population by a vector-borne pathogen and the risk of the host becoming infected. Challenge is a function of many elements of the vector’s biology; risk is often modulated by host resistance and/or acquired immunity. Fixed age-specific prevalences at each level of challenge with different levels of vertebrate host mortality rate (m in the figure) produce population prevalence/challenge curves of different overall shapes.
Distributions of five mosquito species in the Anopheles gambiae complex in Africa, predicted from temporal Fourier-processed satellite data (Box 1) and elevation (global coverage provided by the digital elevation model GTOPO30; http://edcdaac.usgs.gov/gtopo30/README.html ) at a spatial resolution of 0.05°. The colour-coded probabilities of presence effectively indicate the environmental suitability for each species throughout the continent. Symbols indicate sample sites for each species. Between 18° N to 30° S, each species was classed as present only within 0.15° of the sites from which it has been recorded and absent only from similarly sized sites where any of the other species have been recorded. Within these sites, presence pixels (300 for A. gambiae s.s. and A. arabiensis and 100 for the other species) and absence pixels (400 for all species) were chosen at random; additional randomly selected absence pixels were chosen north of 18° N (n=200) and south of 30° S (n=50). Satellite data: middle infrared, land surface temperature and normalized difference vegetation index for 1982–1998 were derived from the National Oceanographic and Atmospheric Administration’s Advanced Very High Resolution Radiometer, and cold cloud duration for 1988–1999 from Meteosat High Resolution Radiometer. Satellite and elevation data for all sample pixels were subjected to k-means clustering within the Statistics Package for the Social Sciences (SPSS, Chicago) to identify up to six natural clusters each of presence and absence pixels for each mosquito species. Within maximum likelihood discriminant analysis, stepwise selection of up to ten variables was applied to maximize predictive accuracy according to the kappa statistic, sensitivity and specificity (Box 1) and to calculate the posterior probabilities with which each pixel belongs to the presence or absence classes within the training set. Sites too different from any of the training set sites are assigned to a ‘no prediction’ class. a, A. arabiensis: 86.0% correct predictions, 7.7% false positives, 6.2% false negatives, sensitivity=0.80, specificity=0.89, κ=0.679 (±0.051 95% confidence interval). b, A. gambiae s.s.: 92.4% correct, 4.2% false positives, 3.4% false negatives, sensitivity=0.89, specificity=0.94, κ=0.826 (±0.039). c, A. quadriannulatus: 99.1% correct, 0.9% false positives, 0% false negatives, sensitivity=1, specificity=0.99, κ=0.961 (±0.029). d, A. melas (West Africa): 98.2% correct, 1.6% false positives, 0.1% false negatives, sensitivity=0.99, specificity=0.98, κ=0.928 (±0.039); and A. merus (East Africa): 98.4% correct, 1.6% false positives, 0% false negatives, sensitivity=1, specificity=0.98, κ=0.933 (±0.037).
Satellite-derived predictions of entomological inoculation rate (EIR) in Africa. EIR data (map inset) were grouped into five approximately equal-sized classes of mean levels of malaria challenge. The same satellite data layers and analytical methods as used in Fig. 2 are used to define the probability with which each continental pixel belongs to one of the five challenge categories. Insufficient training data were available to define EIR in those parts of the continent marked grey. Descriptive accuracies: EIR range 0–4.4, 85.7% (n=21); 5.8–26.6, 81.8% (n=22); 31.0–87.0, 77.3% (n=22); 89.8–255.5, 68.2% (n=22), 259.9–703.4, 95.5% (n=22). Overall κ=0.771 (±0.064).
Amplitude of Fourier harmonics derived from windowed Fourier analysis of malaria cases per month and a range of climatic variables for the period January 1966 to December 1998. a, Malaria cases per month; b, temperature (°C); c, rainfall (mm); data (from ref. 38) from Kericho, Kenya. d, Data from the Multivariate El Niño/Southern Oscillation (ENSO) Index, (http://www.cdc.noaa.gov/~kew/MEI/ ). All data were first de-trended with a 60-point moving average, and the analysis was based upon the difference of the raw data from this trend line. Beginning in 1966, a 12-year window was moved forward one year at a time until 1998 was included. The rapid increase in the amplitudes of the 1-year (red line) and ~3-year (blue line) harmonics of the malaria data have no obvious parallels in the temperature, rainfall or ENSO records, suggesting that these changes are not driven by any element of climate change. The green lines show the results for all other harmonics with periods of between 1 and 12 years.
References
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