Malaria transmission is strongly associated with location. This association has two main features. First, the disease is focused around specific mosquito breeding sites and can normally be transmitted only within certain distances from them: in Africa these are typically between a few hundred metres and a kilometre and rarely exceed 2-3 kilometres. Second, there is a marked clustering of persons with malaria parasites and clinical symptoms at particular sites, usually households. In localities of low endemicity the level of malaria risk or case incidence may vary widely between households because the specific characteristics of houses and their locations affect contact between humans and vectors. Where endemicity is high, differences in human/vector contact rates between different households may have less effect on malaria case incidences. This is because superinfection and exposure-acquired immunity blur the proportional relationship between inoculation rates and case incidences. Accurate information on the distribution of malaria on the ground permits interventions to be targeted towards the foci of transmission and the locations and households of high malaria risk within them. Such targeting greatly increases the effectiveness of control measures. On the other hand, the inadvertent exclusion of these locations causes potentially effective control measures to fail. The computerized mapping and management of location data in geographical information systems should greatly assist the targeting of interventions against malaria at the focal and household levels, leading to improved effectiveness and cost-effectiveness of control.

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Spatial targeting of interventions against malaria.

Since the launch of Landsat-1 28 years ago, remotely sensed data have been used to map features on the earth's surface. An increasing number of health studies have used remotely sensed data for monitoring, surveillance, or risk mapping, particularly of vector-borne diseases. Nearly all studies used data from Landsat, the French Systeme Pour l'Observation de la Terre, and the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer. New sensor systems are in orbit, or soon to be launched, whose data may prove useful for characterizing and monitoring the spatial and temporal patterns of infectious diseases. Increased computing power and spatial modeling capabilities of geographic information systems could extend the use of remote sensing beyond the research community into operational disease surveillance and control. This article illustrates how remotely sensed data have been used in health applications and assesses earth-observing satellites that could detect and map environmental variables related to the distribution of vector-borne and other diseases.

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Remote sensing and human health: new sensors and new opportunities.

Landscape attributes influence spatial variations in disease risk or incidence. We present a review of the key findings from eight case studies that we conducted in Europe and West Africa on the impact of land changes on emerging or re-emerging vector-borne diseases and/or zoonoses. The case studies concern West Nile virus transmission in Senegal, tick-borne encephalitis incidence in Latvia, sandfly abundance in the French Pyrenees, Rift Valley Fever in the Ferlo (Senegal), West Nile Fever and the risk of malaria re-emergence in the Camargue, and rodent-borne Puumala hantavirus and Lyme borreliosis in Belgium. We identified general principles governing landscape epidemiology in these diverse disease systems and geographic regions. We formulated ten propositions that are related to landscape attributes, spatial patterns and habitat connectivity, pathways of pathogen transmission between vectors and hosts, scale issues, land use and ownership, and human behaviour associated with transmission cycles. A static view of the "pathogenecity" of landscapes overlays maps of the spatial distribution of vectors and their habitats, animal hosts carrying specific pathogens and their habitat, and susceptible human hosts and their land use. A more dynamic view emphasizing the spatial and temporal interactions between these agents at multiple scales is more appropriate. We also highlight the complementarity of the modelling approaches used in our case studies. Integrated analyses at the landscape scale allows a better understanding of interactions between changes in ecosystems and climate, land use and human behaviour, and the ecology of vectors and animal hosts of infectious agents.

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Pathogenic landscapes: Interactions between land, people, disease vectors, and their animal hosts

Geographic information systems are powerful automated systems for the capture, storage, retrieval, analysis, and display of spatial data. While the systems have been in development for more than 20 years, recent software has made them substantially easier to use for those outside the field. The systems offer new and expanding opportunities for epidemiology because they allow an informed user to choose between options when geographic distributions are part of the problem. Even when used minimally, these systems allow a spatial perspective on disease. Used to their optimum level, as tools for analysis and decision making, they are indeed a new information management vehicle with a rich potential for public health and epidemiology. Geographic information systems (GIS) are

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On Epidemiology and Geographic Information Systems: A Review and Discussion of Future Directions

Background: An increasing knowledge of the global risk of malaria shows that the nations of the Americas have the lowest levels of Plasmodium falciparum and P. vivax endemicity worldwide, sustained, in part, by substantive integrated vector control. To help maintain and better target these efforts, knowledge of the contemporary distribution of each of the dominant vector species (DVS) of human malaria is needed, alongside a comprehensive understanding of the ecology and behaviour of each species. Results: A database of contemporary occurrence data for 41 of the DVS of human malaria was compiled from intensive searches of the formal and informal literature. The results for the nine DVS of the Americas are described in detail here. Nearly 6000 occurrence records were gathered from 25 countries in the region and were complemented by a synthesis of published expert opinion range maps, refined further by a technical advisory group of medical entomologists. A suite of environmental and climate variables of suspected relevance to anopheline ecology were also compiled from open access sources. These three sets of data were then combined to produce predictive species range maps using the Boosted Regression Tree method. The predicted geographic extent for each of the following species (or species complex*) are provided: Anopheles (Nyssorhynchus) albimanus Wiedemann, 1820, An .( Nys.) albitarsis*, An .( Nys.) aquasalis Curry, 1932, An .( Nys.) darlingi Root, 1926, An .( Anopheles) freeborni Aitken, 1939, An .( Nys.) marajoara Galvao & Damasceno, 1942, An .( Nys.) nuneztovari*, An .( Ano.) pseudopunctipennis* and An .( Ano.) quadrimaculatus Say, 1824. A bionomics review summarising ecology and behaviour relevant to the control of each of these species was also compiled. Conclusions: The distribution maps and bionomics review should both be considered as a starting point in an ongoing process of (i) describing the distributions of these DVS (since the opportunistic sample of occurrence data assembled can be substantially improved) and (ii) documenting their contemporary bionomics (since intervention and control pressures can act to modify behavioural traits). This is the first in a series of three articles describing the distribution of the 41 global DVS worldwide. The remaining two publications will describe those vectors found in (i) Africa, Europe and the Middle East and (ii) in Asia. All geographic distribution maps are being made available in the public domain according to the open access principles of the Malaria Atlas Project.

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The dominant Anopheles vectors of human malaria in the Americas: Occurrence data, distribution maps and bionomic précis

A landscape approach using remote sensing and geographic information system (GIS) technologies was developed to discriminate between villages at high and low risk for malaria transmission, as defined by adult Anopheles albimanus abundance. Satellite data for an area in southern Chiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus abundance data had been collected. The relationships between vector abundance and landscape element proportions were investigated using stepwise discriminant analysis and stepwise linear regression. Both analyses indicated that the most important landscape elements in terms of explaining vector abundance were transitional swamp and unmanaged pasture. Discriminant functions generated for these two elements were able to correctly distinguish between villages with high and low vector abundance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising tool for malaria surveillance programs that depend on labor-intensive field techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where 1) the landscape elements critical to vector survival are known and 2) these elements can be detected at remote sensing scales.

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

Spatial and seasonal variations on Anopheles albimanus larval densities and their plant associations were investigated in larval habitats in southern Mexico between April 1989 and May 1990. Thirty-four plant groups were dominant in larval habitats. Dense larval populations were associated with 3 genera of plants, Cynodon, Echinocloa and Fimbristylis and no larvae were found in habitats with Salvinia and Rhizophora. Low significant positive or negative associations were documented with the other 12 plant genera. Larval habitats were classified according to the morphology of their dominant plants. Higher larval densities were observed in the groups characterized by relatively short emergent vegetation. The distribution of habitat-types within 5 identified vegetation units showed a significantly dependent relationship. For the entire study period, highest larval densities were detected in flooded pasture/grassland vegetation units. For all vegetation units, higher larval densities were found when the dominant plant type covered between 25-50% of the breeding site. The integration of data from habitat-types into vegetation units did not result in loss of information.

R A Meza

Papers

Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

Dynamics of population densities and vegetation associations of Anopheles albimanus larvae in a coastal area of southern Chiapas, Mexico