body top image
Research

Satellite Imagery in the Study and Forecast of Malaria

(continued)


4. Non-Equilibrium Situations

4.1 Seasonality and Intrinsic Dynamics

The position of any site on the horizontal axis of Fig. 1 determines average malaria risk, but seasonal changes in the challenge variables (mosquito abundance and EIR) cause seasonal changes in risk that may be either very pronounced (such as is characteristic of conditions of low average challenge) or strongly buffered by herd immunity responses (at high challenges). At low challenge levels, and because of time delays between the index of challenge and its expression as malaria infections, the risk-challenge relationship will trace an anticlockwise ellipse over time, completing approximately one cycle per year. Remotely sensed data that monitor one or more measure of challenge will therefore be able to predict risk with an appropriate lead time.

In high challenge areas this simple picture is obscured by the longer-term effects of intrinsic disease dynamics, which tend to be uncoupled from the annual seasonality in a way that makes prediction much less accurate. Intrinsic dynamics can produce cycles of disease prevalence in human populations with periods of a few to many years. A recent analysis of long-term records of two mosquitoborne diseases, dengue in Thailand and malaria in Kenya, shows clear evidence of such cycles, with periods in each case of about 3 years.[32] The absence of any sign of equivalent multi-annual cycles in the contemporary meteorological records at each site supports the interpretation that these cycles are driven by intrinsic biotic factors such as host acquired immunity.

The transition from low levels of endemicity, where variation in malaria incidence is apparently driven by extrinsic factors, to higher levels of endemicity, where intrinsic dynamics overrides annual seasonality, may be abrupt (D.J.R., unpublished data). This transition may occur with only modest increases in average mosquito abundance. Above a certain level of endemicity the resulting multi-annual cycles of infection are neither simply, nor easily, linked to either meteorological or satellite data.

4.2 Short- and Long-Term Cycles in Weather Patterns

Longer-term weather cycles such as the El Niño/Southern Oscillation (ENSO) in the Pacific Ocean have been invoked recently to 'explain' outbreaks of malaria and other diseases.[33] Some of these analyses are not statistically robust, while none of them allows an alternative explanation involving intrinsic cycles. This problem is particularly acute because within the past 30-40 years the quasiperiodic ENSO signal has shown a gradual increase in frequency and now has a periodicity of approximately four years.[34] This is close enough to the intrinsic cycles revealed for dengue in Thailand and malaria in Kenya for a casual analysis to suggest a causal link between the two. Before any disease periodicity can be attributed with any certainty to ENSO, a link must be established between the global ENSO index and local weather records (measured directly or remotely) pertinent to the particular vector-borne pathogen.

4.3 Trends and Global Change

The disparity in conclusions about the likely impact of global climate change on malaria distribution in the future [35][36] highlights the significant gaps in our knowledge of this most important tropical disease. Attempts to relate past trends in malaria prevalence to climate records[37] are the critical tests of the real role of climate in the recent history of malaria and therefore its likely impact in the future. Statistical analyses of time series usually require stationarity (the property of constant mean and variance through a series), which is generally achieved by calculating the difference from the overall trend line of each sequential observation. Often, however, it is the trend itself which is of interest and importance.

Windowed Fourier analysis (Box 1 on page 4) of long-term (1966-1998) monthly malaria data from Kericho, Kenya[32][38] shows that the amplitude of many of the component Fourier harmonics has increased over this period; this is most marked in cycles with periods of one and about three years (Fig. 4). Similar analyses of contemporary temperature and rainfall records show no equivalent changes (Fig. 4); rather, if anything there has been a decrease in the amplitude of the same Fourier harmonics. The most parsimonious interpretation of these results is that following a period of aggressive and wide-spread use of chloroquine for fever management, and for some periods as prophylaxis, this drug became less effective at parasite clearance during the mid-late 1980s. Malaria no longer suppressed by the drugs began to show higher-amplitude extrinsic and intrinsic cycles, coupled, respectively, to annual seasonal variation and infection-recovery-immunity effects.

Where climate change is accompanied by changing abundance of mosquitoes, its impact on the levels and periodicity of malaria outbreaks will be far more complex than modelled so far. Although remotely sensed images of seasonal environments can set the scene for long-term studies of disease, such data must be combined with contemporary field data and dynamic models of disease transmission for a full explanation of vector-borne diseases in an ever-changing world.


Figure 4: Amplitude of Fourier harmonics derived from windowed Fourier analysis of malaria cases per month and a range of climatic variables for the period January 1966 to December 1998.

issue picture

a, Malaria cases per month;
b, temperature (°C);
c, rainfall (mm); data (from ref. 38) from Kericho, Kenya.
d, Data from the Multivariate El Niño/ Southern Oscillation (ENSO) Index[43][44] ( http://www.cdc.noaa.gov/people/klaus.wolter/MEI/).

All data were first de-trended with a 60-point moving average, and the analysis was based upon the difference of the raw data from this trend line. Beginning in 1966, a 12-year window was moved forward one year at a time until 1998 was included. The rapid increase in the amplitudes of the 1-year (red line) and ˜3-year (blue line) harmonics of the malaria data have no obvious parallels in the temperature, rainfall or ENSO records, suggesting that these changes are not driven by any element of climate change. The green lines show the results for all other harmonics with periods of between 1 and 12 years.


5. Future Perspectives

Statistical models relating various components of the transmission of malaria parasites to satellite data demonstrate clearly the potential role and importance of remotely sensed imagery to descriptions, explanations and predictions of vector-borne disease. In the face of a world changing in both abiotic and biotic respects, and complex infection and disease dynamics that are not amenable to simple statistical interpretations, we must now take the next step -- the incorporation of satellite data into biological models of pathogen transmission. Studies have already related vector mortality rates and abundance to satellite data,[39] and biological models have been developed for a few vectors[40][41] and vector-borne pathogens.[26]

Extensive satellite coverage coupled with vector-borne pathogen models that are appropriate at a local scale will enable us to build spatially rich, accurate models of vector-borne pathogens. If we can understand transmission dynamics well enough to model the present, we should be able to develop accurate disease early-warning systems in the future. It is clear that the technologies we now have to study these diseases are far better than those available to malariologists in the early years of the last century. The challenge is to make the science of malaria prediction at least as good.

page 1 | page 2 | page 3 | page 4 | top  

filler image
body bottom image