The links between climate and many environmental and vector-borne diseases (VBDs) are felt through the impacts of various climatic components (e.g., temperature, rainfall) on the physiology of pathogens and their vectors. Although there are crude atlases of disease distribution within Africa (Knoch and Schulze, 1956), accurate and verified models that translate these physiological climate-related processes into more detailed maps of disease distribution are scarce. Such maps and models are necessary to set the baseline of current levels and limits of transmission against which projected impacts of climate change can be measured. These changes may include shifts in the distribution of diseases into areas that previously were disease-free or a change in severity at a given location. Although such models now exist (Martens et al., 1995a,b; le Sueur et al., in preparation), most remain hypothetical and largely unverified. However, they may provide good starting points to illustrate the effects of projected climate change. In the case of malaria, a continental effort-Mapping Malaria Risk in Africa (MARA/ARMA)-is now underway; this effort will provide a data base of disease distribution and severity that can be used to verify climate-induced processes. No such parallel efforts, however, currently are underway for other diseases of the African continent that may be affected by climate change (e.g., arboviruses, trypansomiasis, and schistosomiasis).
In Africa, VBDs are major causes of illness and death. Table
2-12 provides global estimates of the number of people at risk from and
the number of people who currently are infected by major VBDs. Currently, the
distribution of most VBDs remains well within the climatic limits of their vectors.
The extent to which disease transmission potential shifts geographically in
response to shifts in vector distribution following climate change will depend
partly on how human activities modify local ecosystems (McMichael et al., 1996).
Rodent-borne diseases that could be affected by climate change include plague
and hantavirus pulmonary syndrome. In a warmer and more urbanized world, rodent
populations-which act as pathogen reservoirs and as hosts for the relevant arthropod
vectors-will tend to increase. Thus, incidences of these diseases can be expected
to rise (Shope, 1991).
Projected increases in the interannual variability of climate would have marked
implications for the impact of seasonal epidemic diseases such as malaria. In
general, control and mitigation activities for such diseases are planned around
mean expected levels in any one year. Significant interannual variation impedes
intervention and mitigation because of the impact on national budgets (which
plan for mean circumstances) and lags that occur in relation to responses to
climatically induced epidemic situations. In addition, such variation results
in intermittent exposure of nonimmune populations-resulting in high levels of
morbidity and mortality. The recent degree of variability is clearly illustrated
in Table 2-13, which shows data for four southern African
countries. This variability highlights the need for more climate-based forecasting
systems capable of predicting such interannual variations with a lead time that
allows health authorities to respond in a timely manner with preparatory/preventative
measures (Jury, 1996; le Sueur and Sharp, 1996).
Table 2-13: Interannual variability of malaria (number of cases) within the southern Africa region. | |||||
|
|||||
Country |
1992
|
1993
|
1994
|
1995
|
1996
|
|
|||||
Botswana | |||||
(confirmed)
|
415
|
14,615
|
5,335
|
2,129
|
19,340
|
(unconfirmed) (1)
|
4,293
|
40,722
|
24,256
|
15,470
|
49,315
|
Namibia (1) |
238,592
|
386,215
|
407,863
|
286,407
|
353,593 (2)
|
South Africa |
2,886
|
13,330
|
10,298
|
9,287
|
29,206
|
Zimbabwe (1) | 420,137 | 877,734 | 797,659 | 721,376 | 1,585,850 |
|
|||||
(1) Clinically diagnosed. (2) Incomplete |
Other reports in this collection |