Elsevier

The Lancet

Volume 362, Issue 9398, 29 November 2003, Pages 1792-1798
The Lancet

Articles
Potential effect of climate change on malaria transmission in Africa

https://doi.org/10.1016/S0140-6736(03)14898-2Get rights and content

Summary

Background

Climate change is likely to affect transmission of vector-borne diseases such as malaria. We quantitatively estimated current malaria exposure and assesed the potential effect of projected climate scenarios on malaria transmission.

Methods

We produced a spatiotemporally validated (against 3791 parasite surveys) model of Plasmodium falciparum malaria transmission in Africa. Using different climate scenarios from the Hadley Centre global climate model (HAD CM3) climate experiments, we projected the potential effect of climate change on transmission patterns.

Findings

Our model showed sensitivity and specificity of 63% and 96%, respectively (within 1 month temporal accuracy), when compared with the parasite surveys. We estimate that on average there are 3·1 billion person-months of exposure (445 million people exposed) in Africa per year. The projected scenarios would estimate a 5–7% potential increase (mainly altitudinal) in malaria distribution with surprisingly little increase in the latitudinal extents of the disease by 2100. Of the overall potential increase (although transmission will decrease in some countries) of 16–28% in person-months of exposure (assuming a constant population), a large proportion will be seen in areas of existing transmission.

Interpretation

The effect of projected climate change indicates that a prolonged transmission season is as important as geographical expansion in correct assessment of the effect of changes in transmission patterns. Our model constitutes a valid baseline against which climate scenarios can be assessed and interventions planned.

Introduction

90% of malaria cases occur in Africa.1 In the past decade, the incidence of malaria has been escalating at an alarming rate. There is an increasing interest in the mapping and predictive modelling of the distribution, intensity, and seasonality of malaria transmission.2, 3, 4, 5, 6 Climate change is likely to have various effects on health, including changes in distribution and seasonal transmission of vector-borne diseases.7 The extent of these effects, however, continues to generate intense debate,8, 9 especially in the projected effect of climate change on the global distribution of malaria, in which different approaches have resulted in widely varying estimates. A general issue facing all researchers has, however, been the absence of comprehensive, good-quality empirical data to validate the models used. The link between climate and malaria distribution has long been established. Sustained transmission depends on favourable environmental conditions for both vector and parasite. The effect of temperature on the duration of the sporogonic cycle of the malaria parasite and vector survival10, 11 is particularly important.

Several methods have been used to estimate changes in the worldwide distribution of malaria in scenarios of global climate change. One approach relies on a biological model that predicts a large increase in global malaria potential.12, 13 Some have criticised biological models on the basis that crucial parameters and their relations with environmental factors have not yet been quantified.14 Thus, biological models have used only a limited number of covariates, and doubts have been raised about the qualitative validity of some results.15 An alternative approach, based on a statistical model derived from the current malaria distribution projects little change in distribution.14 The use of current malaria distribution to derive the model resulted in areas that are climatically suitable for transmission but in which malaria has been eradicated (eg, northern parts of Australia), skewing the results. Generic disadvantages of worldwide or continent-wide statistically-driven models are that data sets used to statistically develop the models are often of uncertain accuracy, models are not easily reproducible (ie, results vary with training data and methods used), and the results are often applicable only to national or subregional scales. We use a large set of parasite surveys done throughout Africa to produce a spatiotemporally validated model of malaria transmission and project the effect of three climate scenarios by Hadley Centre global climate model (HadCM3) climate experiments.

Section snippets

Data

We used mean long-term monthly rainfall and temperature data as the basis for the seasonality model.16 The gridded surfaces were based on weather station data from 1920 to 1980 and have a spatial resolution of 0·05°. The temperature data have SEs of 0·5°C and monthly mean precipitation data have errors of 10–30%.

The population data we used was an interpolated gridded surface17 of resolution 0·042° with 1995 population estimates. Data were interpolated with a spatial interaction model that

Results

Our seasonality model estimates that on average there are 3·1 billion person-months of exposure to malaria (445 million people exposed) in Africa every year (table 2). The spatial and temporal validation of the predicted current malaria distribution (figure 2) was undertaken with positive (n=3199) and negative (n=592) parasite surveys of 1 month duration. The model showed a sensitivity (ie, the ability of the model to accurately predict areas of transmission to within a month) of 63% (95% CI

Discussion

We have produced a spatiotemporally validated malaria transmission model for Africa and projected changes in transmission patterns in differing climate scenarios. Our findings have important implications for malaria control in Africa since both the duration and timing of malaria transmission season are important to inform efforts in malaria control. The duration of the season will affect the dynamics of transmission, with longer seasons allowing heightened transmission and high levels of

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