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. 2004 Feb 24;101(8):2375-80.
doi: 10.1073/pnas.0308714100.

Association between climate variability and malaria epidemics in the East African highlands

Guofa Zhou  1 , Noboru Minakawa, Andrew K Githeko, Guiyun Yan
Affiliations

Association between climate variability and malaria epidemics in the East African highlands

Guofa Zhou et al. Proc Natl Acad Sci U S A. .

Erratum in

  • Proc Natl Acad Sci U S A. 2004 Sep 14;101(37):13694

Abstract

The causes of the recent reemergence of Plasmodium falciparum epidemic malaria in the East African highlands are controversial. Regional climate changes have been invoked as a major factor; however, assessing the impact of climate in malaria resurgence is difficult due to high spatial and temporal climate variability and the lack of long-term data series on malaria cases from different sites. Climate variability, defined as short-term fluctuations around the mean climate state, may be epidemiologically more relevant than mean temperature change, but its effects on malaria epidemics have not been rigorously examined. Here we used nonlinear mixed-regression model to investigate the association between autoregression (number of malaria outpatients during the previous time period), seasonality and climate variability, and the number of monthly malaria outpatients of the past 10-20 years in seven highland sites in East Africa. The model explained 65-81% of the variance in the number of monthly malaria outpatients. Nonlinear and synergistic effects of temperature and rainfall on the number of malaria outpatients were found in all seven sites. The net variance in the number of monthly malaria outpatients caused by autoregression and seasonality varied among sites and ranged from 18 to 63% (mean=38.6%), whereas 12-63% (mean=36.1%) of variance is attributed to climate variability. Our results suggest that there was a high spatial variation in the sensitivity of malaria outpatient number to climate fluctuations in the highlands, and that climate variability played an important role in initiating malaria epidemics in the East African highlands.

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Figures

Fig. 1.
Fig. 1.
Meteorological and number of monthly malaria outpatients time series and epidemic alerts in Nandi District Hospital, western Kenya. (a) Maximum monthly temperature. (b) Minimum monthly temperature. (c) Monthly rainfall. (d) Annual variance in monthly maximum and minimum temperature and rainfall (rainfall variance was scaled by division by 2,000). (e) The number of monthly malaria outpatients. Dashed lines stand for the months that epidemic alerts were declared by the Cullen's method. Meteorological parameters were represented by standardized anomaly relative to the 1961–1990 mean for each site. See Table 1 for source of number of malaria outpatients data. For a comprehensive list of time series data of other study sites, see supporting information.
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