1. Space, the First Frontier
Space is the stage where factors leading to disease take place.
Landscape epidemiology, health geography, spatial epidemiology, and landscape ecology
1
are disciplines that share a fundamental concept: space.
Space as herein defined is the multidimensional extent in which elements and historical
events—geological, physiographical, ecological, climatic, economical, and cultural—concur
and interact with humans to influence the presence, development, activity, and longevity
of pathogens, reservoirs, and vectors, leading to different patterns of infection
and disease.
Disease is a spatially-determined phenomenon [1–3]. The correct identification of
spatial risk factors plays a key role in prediction, prevention, and control of disease
[4–7].
The analysis of space and its role in diseases has been occupying thinkers since Hippocrates
circa 400 B.C. Terms like tropical diseases, malaria, and American trypanosomiasis
just to name a few, have geographically-oriented denominations that immediately refer
to spaces where transmission occurs
2
.
According to Hippocrates, an endemic disease is determined by the nature of a certain
place. The term endemic
3
entails in itself this concept. Hippocrates' ecological concept of disease was brought
up again by Galen in the early Christian era and passed untouched through the modernization
of science during the Renaissance.
2. The Study of Space
Spatial analysis is concerned with the geographic space, that is, observations that
correspond to locations in space that capture their proximity in the real world. The
interrelation between entities increases with proximity in the real world and their
representation in geographic space and assessment using spatial analysis techniques
are appropriate (Tobler's first law of geography, [8]) and in accordance with the
concept of spatial dependence that forms the foundation of spatial analysis.
In 1939, Pavlovsky structured the theory of the natural nidality
4
of transmissible diseases, known outside the Soviet Union only by late 1950s [2, 3].
Pavlovsky's theory consisted of 3 axioms: (1) diseases tend to be limited geographically;
(2) this spatial variation arises from underlying variation in the physical and/or
biological conditions that support the pathogen and its vectors and reservoirs; (3)
if those abiotic and biotic conditions can be delimited on maps, then both contemporaneous
risk and future change in risk should be predictable [9].
A landmark on spatial analysis was Snow's 1854 map of cholera deaths and the water
pumps that supplied the city. The superposition of cholera cases and a main water
pump gave support to his hypothesis that cholera was spread by the water [10].
Spatial analysis comprises a set of generic exploration methods and the visualization
of data in the form of maps.
Nonetheless, spatial analysis is neither a technology for making maps nor a map is
the sole objective to be achieved. The interaction with mapped variables resulting
from spatial analysis is more challenging than the interaction with traditional maps
and spatial records. Spatial analysis has changed our perspective of viewing a map.
It has moved mapping from a historical role of input provider to a dynamic and essential
element in the decision-making process [11].
Spatial analysis exploration and visualization methods allow not only the visual description
of the distribution of variables but also the identification of patterns in the spatial
distribution making it easier to comprehend the phenomena underlying these observations.
Through these procedures it is possible to select the most accommodating inferential
model [12], to choose the best explanatory hypothesis and propose control scenarios
supported by the spatial relationships observed.
3. Spatial Studies Tools
Visual inspection of spatial distribution of data allows apprehending existing patterns.
The translation of these patterns into a system of theoretical significance is an
important tool in the investigation of the disease process.
Currently, spatial studies of disease and vectors involve the use of computational
analyses and representation of geographic data using the so-called geographic information
systems-GIS or spatial analysis tools, as in the broader sense used in this text.
These spatial analysis tools perform the computational treatment of georeferenced
data points, lines, and areas and store their attributes in relation to the earth
surface and represented in a cartographic projection [12]. Advances in remote sensing,
global positioning systems, computer software, and hardware led to the creation of
powerful data exploratory tools.
A spatial-related geographic database is composed of georeferenced data input and
integration, graph, and image processing functions, visualization, and plotting, spatial
analysis tools, data storage, and retrieval in an organized form.
After being subjected to visualization techniques, hypotheses on spatial behavior
of data are challenged to validation and corroboration through spatial analysis and
theoretical models.
Besides being able to create static risk maps based on distributions of vectors, reservoirs
and disease incidence, spatial analysis can model spatiotemporal dynamics and show
how the spatial distribution of infectious diseases changes through space and time.
Where field data are unavailable, predictors of disease risk can be applied.
Spatial analysis is both quantitative and qualitative. Neither quantitative nor qualitative
methods are end tools. Quantitative spatial analyses with predictive models and qualitative
scrutiny and theories help to gain a better understanding of visualized data and the
underlying processes within.
Nature of spatial data affects the type of spatial analysis to be employed and interpretation.
Data selection, data cleaning, and preprocessing and, moreover interpretation of results
are all part of the subjective qualitative interpretation. Subjective analysis should
be ascertained as an important tool. The testing of different analyses should be extensively
used to aggregate knowledge on the problem. Spatial analysis results should be used
to corroborate or reject findings and applied to mathematical models for interpretation
and theory construction. These in turn, will be used for building up knowledge on
disease dynamics and ultimately for practical control issues like policy and management
decisions and, for checking particular interventions.
4. In This Issue
The papers comprised in this special issue contain up-to-date methods of spatial analyses
applied to the study of diseases and vectors. These papers investigate spatial and
temporal scales, and develop risk maps and models aiming to increase our understanding
as well as supporting decisions in control programs.
In order to reduce malaria transmission in Zambia, a spatial study was developed to
guide the implementation of effective vector control measures, and increase the understanding
of the interactions between epidemiological and entomological malaria transmission
determinants and the impact of interventions (E. Chanda et al. in this special issue).
Monitoring the impact of vector control through a spatial-based decision support system
revealed spatial variations in the prevalence of infection and vectors which are susceptible
to insecticides. It also enabled the characterization of the spatial heterogeneity
and the identification of areas with reduced parasitaemia and increased insecticide
resistance. The spatial-based decision support system proposed by E. Chanda et al.
provided opportunity for rational policy formulation and cost effective utilization
of limited resources for enhanced malaria vector control.
Major spatial changes induced by man may lead to disequilibrium that in turn may result
in human disease. This is the hypothesis raised by Paula et al. (in this special issue).
The closure of a dam in São Paulo, Brazil, favoured the proliferation of aquatic weeds,
the main habitat of Mansonia mosquito species (M. B. de Paula et al. in this special
issue). This event led to a dramatic increase of the Mansonia humeralis population,
from 3 to >50%. Ma. humeralis is a persistent biter provoking nuisance in the human
population and potentially facilitates the transmission of arboviruses. A spatial-oriented
sustainable entomological control was advised for this area.
Although Brazil was declared free from Chagas disease transmission by the domestic
vector Triatoma infestans by the World Health Organization in 2006, vector-transmitted
human acute cases are still being registered (R. Gurgel-Gonçalves et al. in this special
issue). In order to assess Chagas disease transmission risk, distribution models for
62 Brazilian triatomine species were generated (R. Gurgel-Gonçalves et al. in this
special issue). Although most actual occurrences were recorded in open areas of the
Brazilian savannah (cerrado and caatinga), spatial analyses and distribution models
showed that Brazil is, as its most, vulnerable to Chagas vector-borne transmission.
Spatial analysis was applied in a study of Biomphalaria snail species, which are the
intermediate hosts of Schistosoma mansoni in Minas Gerais, Brazil, to optimize resource
allocation (R. J. P. S. Guimarães et al. in this special issue). Kriging showed to
be a rather robust tool since its results presented a good agreement with the field
findings. This tool allowed the delimitation of the Biomphalaria distribution, characterizing
the uncertainty of areas at risk.
Buruli ulcer is a debilitating human skin disease with an unknown transmission mode
with epidemiological data linking it to swampy areas. Data available suggest that
aquatic insects play a role in the dissemination and/or transmission of this disease.
However, aquatic insect biodiversity and biology in Africa remain poorly documented.
Entomological survey in Bankim, Cameroon, an area recently described as endemic for
Buruli ulcer was conducted in order to identify the commonly occurring aquatic bugs
and document their relative abundance, diversity, and spatial distribution (S. M.
A. Ebong et al. in this special issue). Abundance, distribution and diversity of aquatic
bugs varied according to type of aquatic environments and maybe used for future risk
maps assessment.
Control actions for visceral leishmaniasis in Minas Gerais, Brazil, showed that the
use of an automated database with geoprocessing was important to guide control measures
(L. Saraiva et al. in this special issue). In fact, the use of spatial analysis tools
promoted greater efficiency in the decision making and planning activities especially
for urban areas where many of the disease characteristics are unknown.
Spatial-temporal analysis of the abundance of phlebotomines vectors of tegumentary
and visceral leishmaniasis was performed in Argentina compared spatio-temporal scales
(M. G. Quintana et al. in this special issue). Microscale, mesoscale, and macroscale
analyses resulted in different spatial observations. These observations raised the
awareness of scale choice and consistency in conclusions from spatial studies. Scales
from microfocal to regional, although within each other in increasing order, require
questions, resolution, data quality, and different analytical tools, to support the
conclusions appropriate to each scale.
5. Space in the Future
Simple interactions among proximal entities can lead to complex, persistent, and functional
spatial entities at higher aggregate levels. Spatial studies of diseases and vectors
seem to be an excellent platform from which to explore these issues.
Spatial data comes in many varieties and it is not easy to arrive at a system of classification
that is simultaneously exclusive, exhaustive, imaginative, and satisfying [13].
Innovative research on spatial analysis has been building newer insights into fundamental
issues as structure of theories, models, technologies, and new methods of representation
that go beyond earlier GIS models, giving birth to new techniques for addressing uncertainty
[14].
The increasing ability to capture and handle geographic data means that spatial analysis
is occurring within increasingly data-rich and growing analytical power environments.
This wealth of new processing capabilities provides an opportunity to address complex
spatial issues in entirely new ways [11].
The use of spatial models to generate potential distribution and risk maps, followed
by careful assessment of models, could lead to increment in knowledge of the different
properties of a system at different levels of aggregation and in different study fields
to target interventions to prevent, manage, and control disease [9].
In the search for valid and reliable conclusions a variety of exploratory and confirmatory,
qualitative and quantitative procedures are being developed daily.
The incorporation of spatial analytical functionality within commercial and open-source
GIS, the linkage of specialized statistical and other analytical modules have attained
a conspicuous progress in spatial studies [15].
Nonetheless, the pursuit for methods and tools that allow more specific and ever demanding
treatment of space in empirical applications in many sciences, in measurement, in
stressing space-time dynamics, in hypothesis development and in validation of theoretical
constructs will never cease to exist. This pursuit was valid a decade ago [15] and
still is today.
We have started witnessing new map forms and processing environments.
New maps forms go beyond the 2D paper map. Users often require being able to have
spatial information on a 3D view of the terrain. Virtual reality can transform the
information from polygons to objects near photographic realism [11]. A four dimension
spatial analysis tool system (3D plus time) is currently the major challenge. Currently,
visualization of time is only possible by animation of a series of map layers.
Among the new processing environment are full integration of the global positioning
system and remote sensing imagery with spatial analysis in real-time applications.
Typical commercial GIS toolbox software products are being substituted by web spatial
analysis services where users customize their own views.
High-level applications have been initiated by the free release of data in the web
and the ability of using analytical tools and display capabilities from different
sites resulting in complex data manipulation which however does not require specific
GIS knowledge from the end user. In this new environment, the user focuses on the
spatial logic of a solution and is hardly aware that spatial analysis is even involved
[11].
A combination of spatial studies innovations derived from other sciences and interdisciplinary
expertise might lead to advancements farfetched from currently used tools and observed
outcomes. These spatial studies will most likely take us closer and closer to unveil
the natural complexity of components of disease in space.
Maria Goreti Rosa-Freitas
Nildimar Alves Honório
Cláudia Torres Codeço
Guilherme Loureiro Werneck
Nicolas Degallier