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Satellite Imagery in the Study and Forecast of Malaria

David J. Rogers, Sarah E. Randolph, Robert W. Snow, and Simon I. Hay

The authors are affiliated with the following: TALA Research Group and Oxford Tick Research Group, Department of Zoology, University of Oxford; Kenya Medical Research Institute/Wellcome Trust Collaborative Programme, Nairobi, Kenya; and the Centre for Tropical Medicine, University of Oxford, John Radcliffe Hospital.

More than 30 years ago, human beings looked back from the Moon to see the magnificent spectacle of Earthrise. The technology that put us into space has since been used to assess the damage we are doing to our natural environment and is now being harnessed to monitor and predict diseases through space and time. Satellite sensor data promise the development of early-warning systems for diseases such as malaria, which kills between 1 and 2 million people each year.

1. Introduction

Malaria caused by Plasmodium falciparum parasites exacts its greatest toll in sub- Saharan Africa, where it is one of the largest causes of morbidity and mortality, creating a significant barrier to economic development. Furthermore, this public health burden is increasing globally,[1] exacerbated by failure of existing affordable drugs, population growth against declining per capita expenditure on health, human migration and poverty. As a step towards reversing this trend, there is growing interest in the mapping and predictive modelling of the geographical limits, intensity and dynamics of the risk of malaria infection, using new tools of surveillance. An unprecedented amount of information on environmental conditions, remotely sensed by satellite sensors, is now available at temporal and spatial resolutions to match our epidemiological questions.

Here we show how these tools are used to investigate the factors that drive the dynamics of vector populations and malaria parasite transmission. Because mosquito population processes and malaria incubation periods in vectors, for example, vary with temperature and moisture conditions on the ground, remotely sensed images of seasonal climate are powerful predictors of mosquito distribution patterns and average levels of transmission of malaria parasites by these vectors. Patterns of infection vary through time owing to extrinsic (for example, climate) and intrinsic (for example, immunity) effects. The balance of these factors depends upon the levels of malaria transmission in each place and will change over time with resistance to control of parasites and vectors. Early-warning systems, therefore, will require models that incorporate both intrinsic and extrinsic factors.

2. Extrinsic and Intrinsic Drivers of Malaria

Diseases caused by vector-borne pathogens vary in magnitude through space and time much more than directly transmitted pathogens, because their innate capacity to increase is usually much higher. This is expressed as the basic reproductive number R0:


where m is the vector-host ratio; a is the vector biting rate; b,c are transmission coefficients from vertebrate to vector and vice versa; µ is the vector mortality rate; T is the extrinsic incubation period of the parasite in the vector; and r is the rate of recovery in the vertebrate. Values of R0 for vector-borne pathogens reach hundreds, or even thousands, compared with a typical value of <10 for directly transmitted pathogens.[2] Changes in R0 are most sensitive to changes in variables that appear as powers or exponents (a, µ and T).[3][4]

R0 is reduced to RE, the effective reproductive number that approximates to 1.0 at equilibrium, by population acquired immunity and other processes that either limit vertebrate infectivity for mosquitoes or decrease coefficients of transmission between vertebrates and vectors. Thus, although the increase of vector-borne pathogens is most strongly influenced by the six (out of seven) vectorrelated factors in the R0 equation, their regulation is effected principally by vertebrate factors. However, because acquired vertebrate immunity depends upon the level and frequency of exposure to parasites since birth, it should be possible to define the dynamics of many vector-borne pathogens solely on the basis of the current and previous history of the vector population.

Although invertebrate vectors are strongly influenced by variable climate (that is, abiotic or extrinsic factors),[5] vertebrate host immunity effects (biotic or intrinsic factors) can produce characteristic cycles of infection even in the absence of variation in environmental conditions.[6] The interaction of extrinsic and intrinsic factors results in the characteristic waxing and waning of many vector-borne infections, on timescales from weeks to decades;[7][8] these cycles may be suppressed, but are rarely totally eliminated, by control and intervention efforts. Early attempts to predict malaria outbreaks in India and Pakistan revealed an understanding of this interaction:[9] a combination of the absence of malaria in the preceding 5 years, rainfall anomalies from July to August, and the local prices of wheat (thought to reflect the nutritional state of the human population) was used to predict the amount and distribution of malaria in 1921 with 'considerable precision.'[10] This early promise of accurate forecasting of malaria outbreaks was implemented successfully in the subcontinent for almost 25 years[11] until the introduction of insecticides and cheap drugs in the 1950s and 1960s, when forecasting was no longer felt necessary.[12] There have been no effective early-warning systems for malaria, or indeed for any vectorborne diseases, since that time.[13]

3. New Prospects for Malaria Early-Warning Systems

With a breakdown in effectiveness of the 'cures' found for malaria in the 1950s and 1960s, there has been a global resurgence of malaria1,[14][15] and renewed interest in the concept of malaria early warning.[13][16][17] But instead of rain gauges and wheat prices, we now have an armoury of information on environmental conditions, remotely sensed from satellite sensors, that has been related to the dynamics of vector populations and pathogen transmission.[18][19] Until we can dissect quantitatively the roles played by extrinsic and intrinsic factors, however, we cannot use these new tools to forecast outbreaks. First, therefore, we focus on what satellite imagery can tell us about the environmental prerequisites for malaria transmission in the equilibrium situation.

3.1 The Risk-Challenge Relationship for Malaria

The hypothetical relationship between the challenge presented to the human population by a vector population and the resulting incidence or prevalence of infection, each assuming stable conditions, is complex and nonlinear (Fig. 1). The humped curve is determined by interactions between transmission rates, the rate of development and duration of temporary acquired immunity in the vertebrate host population, and the age structure of the latter. But the precise shape of the relation, and its implications for malaria control, are controversial,[20][21] not least because of a shortage of good quality field data available for its determination. (The relationship between clinical disease and challenge may be different from that shown in Fig. 1, most obviously in areas of high challenge.) The data that do exist for Africa, now being gathered together in the ambitious Mapping Malaria Risk in Africa/Atlas du Risque de Malaria en Afrique collaboration,[22][23] may nevertheless be used first to examine the 'challenge' axis of Fig. 1.

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Figure 1: Hypothetical relationship between the challenge to a host population by a vector-borne pathogen and the risk of the host becoming infected. Challenge is a function of many elements of the vector's biology; risk is often modulated by host resistance and/or acquired immunity. Fixed age-specific prevalences at each level of challenge with different levels of vertebrate host mortality rate (m in the figure) produce population prevalence/challenge curves of different overall shapes.

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