The Global Spread of Malaria in a Future, Warmer World

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Science  08 Sep 2000:
Vol. 289, Issue 5485, pp. 1763-1766
DOI: 10.1126/science.289.5485.1763

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The frequent warnings that global climate change will allow falciparum malaria to spread into northern latitudes, including Europe and large parts of the United States, are based on biological transmission models driven principally by temperature. These models were assessed for their value in predicting present, and therefore future, malaria distribution. In an alternative statistical approach, the recorded present-day global distribution offalciparum malaria was used to establish the current multivariate climatic constraints. These results were applied to future climate scenarios to predict future distributions, which showed remarkably few changes, even under the most extreme scenarios.

Predictions of global climate change have stimulated forecasts that vector-borne diseases will spread into regions that are at present too cool for their persistence (1–5). For example, life-threatening cerebral malaria, caused by Plasmodium falciparum transmitted by anopheline mosquitoes, is predicted to reach the central or northern regions of Europe and large parts of North America (2, 4).falciparum malaria is the most severe form of the human disease, causing most of the ∼1 million deaths worldwide among the ∼273 million cases in 1998 (6). Despite these figures, the epidemiology of malaria, like many other vector-borne tropical diseases, remains inadequately understood. Only the most general of maps for its worldwide distribution are available (7), and its global transmission patterns cannot be modeled satisfactorily because crucial parameters and their relations with environmental factors have not yet been quantified. Most importantly, absolute mosquito abundance has not yet been related to multivariate climate.

Nevertheless, the problem of malaria has led to its being included in most predictions about the impact of climate change on the future distribution of vector-borne diseases (8). These studies, which draw on the forecasts of future climate from various global circulation models (GCMs) (9, 10), generally use only one or at most two climatic variables to make their predictions. Biological models for malaria distribution are based principally on the temperature dependence of mosquito blood-feeding intervals and longevity and the development period of the malaria parasite within the mosquito, each of which affects the rate of transmission (4,11). Those models based on threshold values include a lower temperature threshold, below which all development of the malaria parasite ceases, and an upper limit of mosquito lethality (2). In addition, the suitability (or unsuitability) of habitats for these vectors, which require a minimum atmospheric moisture, is defined by the ratio of rainfall to potential evapotranspiration (2). The output of such models, therefore, represents predicted areas where parasite development within the vector is fast enough to be completed before the vector dies, bounded by limits imposed by habitat suitability (2). The fit of these predictions to the current global malaria situation shows noticeable mismatches in certain places (12); false predictions of presence (e.g., over the eastern half of the United States) are accounted for by past control measures or by “peculiar vector biogeography,” whereas false predictions of absence are dismissed as model errors (2).

Refinements of these biological models (3–5) are based on modifications of an equation describing transmission potential, expressed as the basic reproduction number R 0, which must equal at least 1 for disease persistence (13, 14). For an estimation of the correct value of R 0 from which to predict malaria distribution, absolute, not relative, estimates of all quantities in the equation are needed. Instead, by omitting certain unquantified but important parameters and rearranging the equation (15), a relative measure of “epidemic potential” (EP) [now “transmission potential” (5)] has been derived as the reciprocal of the vector/host ratio required for disease persistence. This predicts a more extensive present-day distribution of malaria than is currently observed (12). The ratio of future EP to present EP is then presented as indicating the relative degree of the future risk of malaria, but this is an inappropriate measure of changing risk because a high ratio may still leaveR 0 < 1.

Until such biological approaches can give accurate descriptions of the current situation of global malaria, they cannot be used to give reliable predictions about the future. Instead, an alternative two-step statistical approach to mapping vector-borne diseases gave a better description of the present global distribution of falciparummalaria and predicted remarkably few future changes, even under the most extreme scenarios of climate change. First, the present-day distribution was used to establish the climatic constraints currently operating on malaria. Then, the results were applied to future GCM scenarios to predict future distribution. Simple maximum likelihood methods were used (16) (Fig. 1A), based on the mean, maximum, and minimum of three climatic variables: temperature, precipitation, and saturation vapor pressure. The match between prediction and reality was significantly closer than that achieved by previous models (12). Some false-positive areas, in eastern South America and Iran, were recorded as of “limited risk” on earlier maps (17), whereas others, in the southern United States and northern Australia, coincided with successful vector control campaigns. Because these predictions were based on present-day malaria maps, the disappearance of malaria in historical times from the edges of its global distribution has effectively been incorporated (18). This itself is a reflection of climatic conditions. In cooler regions, where mosquito life-spans barely exceed extrinsic incubation periods, transmission cycles are inherently more fragile. Not only the range of each climatic variable, but also the covariation between variables proved to be important in setting distributional limits in this model. Biologically, this implies that organisms can cope with extremes of some variables (e.g., temperature) only if others (e.g., humidity) are at certain levels.

Figure 1

(A) Current global map of malaria caused by P. falciparum [yellow hatching, data from (7)] and distribution predicted with maximum likelihood methods (red through green posterior probability scale in key; light blue areas indicate no prediction, i.e., conditions very different from those in any of the sites used to train the analysis). These methods give predictions that are 78% correct, with 14% false-positives and 8% false-negatives (12). (B) Discriminating criteria derived from the current situation were then applied to the equivalent climate surfaces from the high scenario from the HadCM2 experiment (19) which predicts mean global land surface changes of +3.45°, +3.63°, and +3.29°C in mean, minimum, and maximum temperatures, respectively; +1.87 hPa for SVP; and +0.127 mm/day for precipitation by the year 2050. The yellow hatching and the probability scale are the same as in (A). (C) The difference between the predicted distributions given in (A) and (B), showing areas where malaria is predicted to disappear (i.e., probability of occurrence decreases from >0.5 to <0.5) (in red) or invade (i.e., probability of occurrence increases from <0.5 to >0.5) (in green) by the 2050s in relation to the present situation. The gray hatching is the current global malaria map shown in yellow hatching in (A).

The results from this first step were applied to the most widely used GCM scenario of the future, which envisages a 1% annual compound increase in overall greenhouse gas concentrations (9, 19) and a range of climate sensitivity to this increase. Predictions show the future distribution of habitats similar to those wherefalciparum malaria occurs today (Fig. 1B). If introduced, by travel or trading activities, for example, both vectors and parasites could survive in such places. Only a relatively small extension was predicted as compared to the present-day situation: northward into the southern United States and into Turkey, Turkmenistan, and Uzbekistan; southward in Brazil; and westward in China. In other areas, malaria was predicted to diminish (Fig. 1, B and C). The net effect of this on the potential exposure of humans to malaria by the year 2050, compared with the present as modeled in Fig. 1A (20), varied with climate sensitivity to greenhouse gases; for example, there was an increase in exposure of 23 million people (+0.84%) under the HadCM2 “medium-high” scenario (19) or a decrease in exposure of 25 million people (–0.92%) in the HadCM2 “high” scenario (i.e., higher mean temperatures) (Fig. 1C). These changes are modest because covariates limit potential expansion along certain dimensions of environmental space. For example, in the present exercise, a univariate model driven by the minimum of the mean temperature (the single most important predictor in the multivariate fit) would predict more extensive malaria than at present along the southern fringes of the Sahara Desert and an expansion northward into the Sahara, as global warming lifts the cold minimum (nighttime) temperature constraint on mosquito or malaria development. Multivariate models gave more accurate predictions of the present situation and do not predict this expansion, because of the limitations imposed by the covarying rainfall or moisture variables.

Whereas others have raised qualitative doubts about the predicted impact of climate change on malaria (18), the quantitative model presented here contradicts prevailing forecasts of global malaria expansion. It highlights the use of multivariate rather than univariate constraints in such applications and the advantage of statistical rather than biological approaches in situations where biological knowledge is incomplete. Whatever the method adopted, the usefulness of GCMs as a basis for making predictions about the future of biological systems needs further clarification. The current low spatial resolution of such models hides considerable local variation and represents mean conditions across large geographical areas that may not occur in many places within them. Furthermore, the accuracy of GCMs in predicting the covariation of climatic variables, to which biological systems are very sensitive, is unknown.

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