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. 2006 Apr 11;103(15):5829-34.
doi: 10.1073/pnas.0508929103. Epub 2006 Mar 29.

Malaria resurgence in the East African highlands: temperature trends revisited

M Pascual  1 , J A Ahumada, L F Chaves, X Rodó, M Bouma
Affiliations

Malaria resurgence in the East African highlands: temperature trends revisited

M Pascual et al. Proc Natl Acad Sci U S A. .

Abstract

The incidence of malaria in the East African highlands has increased since the end of the 1970s. The role of climate change in the exacerbation of the disease has been controversial, and the specific influence of rising temperature (warming) has been highly debated following a previous study reporting no evidence to support a trend in temperature. We revisit this result using the same temperature data, now updated to the present from 1950 to 2002 for four high-altitude sites in East Africa where malaria has become a serious public health problem. With both nonparametric and parametric statistical analyses, we find evidence for a significant warming trend at all sites. To assess the biological significance of this trend, we drive a dynamical model for the population dynamics of the mosquito vector with the temperature time series and the corresponding detrended versions. This approach suggests that the observed temperature changes would be significantly amplified by the mosquito population dynamics with a difference in the biological response at least 1 order of magnitude larger than that in the environmental variable. Our results emphasize the importance of considering not just the statistical significance of climate trends but also their biological implications with dynamical models.

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Conflict of interest statement

Conflict of interest statement: No conflicts declared.

Figures

Fig. 1.
Fig. 1.
CRU temperature time series at the four locations in the African highlands. The bold line shows the trend obtained for each of these data with SSA. Because the latitude of Muhanga is almost on the boundary between two grid points in the CRU global grid, both the grid points above and those below the latitude of 3.00 S were considered. All results remain the same for these two time series, and we report here only the results for the grid point centered at 2.75 S (a, Kericho; b, Kabale; c, Gikongoro; d, Muhanga).
Fig. 2.
Fig. 2.
SSA. The plots show the different eigenvalues obtained by SSA for each of the four time series ranked by order of importance according to the variance they explain. The dominant eigenvalue in three of the four sites (c, Gikongoro; d, Muhanga; and b, Kabale) corresponds to the trend and is followed by a pair of eigenvalues associated with the seasonal cycle. In Kericho (a), this order is reversed, and the trend corresponds to the subdominant eigenvalue. The trend components account for 10%, 20.5%, 14.7%, and 18% of the total variance, respectively, for Kericho (a), Gikongoro (c), Muhanga (d), and Kabale (b). The respective reconstructed components are shown in Fig. 1 (those for the pair of subdominant eigenvalues in b–d and the dominant pair in a are plotted in Fig. 5). The bars specify the 95% confidence intervals generated with Monte Carlo simulations of red noise. Specifically, the error bars computed for each empirical orthogonal function represent 95% of the range of variance found in the state-space direction defined by that empirical orthogonal function in an ensemble of 999 red-noise realizations. Thus, the bars represent the interval between the 0.5% and 99.5% percentiles, and eigenvalues lying outside this range are significantly different (at the 5% level) from those generated by the red-noise process against which they are tested. The dominant eigenvalues for Gikongoro (c), Kabale (b), and Muhanga (d) and the subdominant eigenvalue for Kericho (a) lie outside this interval and are significantly different from noise. The application of SSA in combination with this red-noise test is known as “Monte Carlo SSA” (38). The ssa toolkit freeware software from www.atmos.ucla.edu/tcd/ssa was used for the analysis. A large order was selected to force a better separation of constituent components mainly at the lower frequencies (22) (a, Kericho; b, Kabale; c, Gikongoro; d, Muhanga).
Fig. 3.
Fig. 3.
RD in mosquito abundances (a and b) and temperature (c and d) for two sites, Kericho (a and c) and Kabale (b and d). For mosquito abundances, the RD time series were computed for each of the 100 stochastic realizations of simulated rainfall (see Supporting Text, which is published as supporting information on the PNAS web site). The values shown here correspond to one representative simulation for each site. Gikongoro and Muhanga show patterns (not shown here) of similar magnitude to those for Kericho, because their mean temperatures are also similar. Kabale’s temperatures are colder and result in a much larger amplification of the temperature difference. In either case, the RDs between the original and detrended temperatures (c and d) are at least 1 order of magnitude smaller than the RDs they generate in the simulated mosquito abundances.
Fig. 4.
Fig. 4.
Sensitivity analysis for the mean (a and b) and maximum (c and d) of RD values in Kericho (a and c) and Kabale (b and d), as a function of larval survival and development rate in the mosquito population model. The filled circles correspond to the combination of parameters used in Fig. 3. Mosquito populations decay exponentially for parameter combinations lying in the white area at the bottom left of each graph.
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