# Spatial Studies on Vector-Transmitted Diseases and Vectors

Maria Goreti Rosa-Freitas, Nildimar Alves Honório, Cláudia Torres Codeço, Guilherme Loureiro Werneck, Nicolas Dégallier

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Maria Goreti Rosa-Freitas, Nildimar Alves Honório, Cláudia Torres Codeço, Guilherme Loureiro Werneck, Nicolas Dégallier. Spatial Studies on Vector-Transmitted Diseases and Vectors. Journal of Tropical Medicine, 2012, 2012, pp.573965. 10.1155/2012/573965. hal-00758278

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Editorial

Spatial Studies on Vector-Transmitted Diseases and Vectors

Maria Goreti Rosa-Freitas,<fnref n="1" /> Nildimar Alves Honório,<fnref n="1" /> Cláudia Torres Codeço,<fnref n="2" />
Guilherme Loureiro Werneck,<fnref n="3" /> and Nicolas Degallier<fnref n="4" />

Correspondence should be addressed to Maria Goreti Rosa-Freitas, goreti@ioc.fiocruz.br

Received 10 May 2012; Accepted 10 May 2012

Copyright © 2012 Maria Goreti Rosa-Freitas et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**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<fnref n="1" /> are disciplines that share a fundamental concept: space.

Space as herein defined is the multidimensional extent in which elements and historical events—geological, physicalographical, 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<fnref n="2" />.

According to Hippocrates, an endemic disease is determined by the nature of a certain place. The term endemic<fnref n="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<fnref n="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.

<footnote n="1">¹Laboratório de Transmissores de Hematozoários, Fundação Oswaldo Cruz, Avenida Brasil 4365 Manguinhos, 21045-900 Rio de Janeiro, RJ, Brazil</footnote>

<footnote n="2">²Programa de Computação Científica, Fundação Oswaldo Cruz, 21045-900 Rio de Janeiro, RJ, Brazil</footnote>

<footnote n="3">³Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, 20550-900 Rio de Janeiro, RJ, Brazil</footnote>

<footnote n="4">⁴IRD UMR182, Laboratoire d'Océanographie et du Climat, Expérimentation et Approches Numériques (LOCEAN) Tour 45-55, 4e étage, case 100, 4 place Jussieu, 75252 Paris Cedex 5, France</footnote>
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 tradi-
tional 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 relation-
ships 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, hypo-
theses 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 dis-
tributions 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 under-
standing 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 sub-
jective 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 pol-
icy 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 under-
standing 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 characteri-
zation of the spatial heterogeneity and the identification of
areas with reduced parasitaemia and increased insecticide
resistance. The spatial-based decision support system pro-
posed 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 dis-
equilibrium 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 pro-
liferation 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 humer-
alis* 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 spa-
tial-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 Brazil-
ian savannah (*cerrado* and *caatinga*), spatial analyses and
distribution models showed that Brazil is, as its most, vul-
nerable 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 allo-
cation (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 repre-
sentation that go beyond earlier GIS models, giving birth to
new techniques for addressing uncertainty [14].

The increasing ability to capture and handle geo-
graphic data means that spatial analysis is occurring within

increasingly data-rich and growing analytical power environ-
ments. 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 distri-
bution 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 spe-
cialized 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 process-
ing 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 infor-
mation from polygons to objects near photographic realism
[11]. A four dimension spatial analysis tool system (3D plus
time) is currently the major challenge. Currently, visualiza-
tion of time is only possible by animation of a series of map
layers.

Among the new processing environment are full inte-
gration 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 solu-
tion 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
## Endnotes

1. *Ecology.* (Greek *oikos* = house, dwelling place, habitation; *logia* = study of) The study of the relationships between living organisms and their environment. The term *Ökologie* was coined in 1866 by the German scientist Ernst Haeckel (1834–1919) [16]. *Landscape*. A portion of land or territory which the eye can comprehend in a single view, including all the objects it contains [17]. A heterogeneous land area composed of a cluster of interacting ecosystems that is repeated in similar form throughout [18]; an area of land containing a mosaic of habitat patches, often within which a particular focal habitat patch embedded [19] relevant to the phenomenon under consideration at any scale where each species is likely to respond to more than one scale [20]. Originally introduced as a painters’ term c. 1600 as a painting representing natural scenery, old English *landscape*, Germany *landschaft*, old Norwegian *landskapr*; the meaning tract of land with its distinguishing characteristics is from 1886 [16]. *Geography*. (Greek *geo* = earth, *graphia* describe-write) The study of lands, features, inhabitants and phenomena of Earth. First used by Eratosthenes (276–194 bC) [21]. *Habitat*. Latin = it inhabits. An ecological or environmental area that is inhabited by a particular organism. The natural environment in which an organism lives, or the physical environment that surrounds (influences and is utilized by) a species population [22]. As a technical term in Latin texts on English flora and fauna, literally it inhabits in 1762. From the verb *habitare* = to live, dwell. General sense of dwelling place is first attested in 1854 [16]. *Environment*. The circumstances, objects, or conditions by which one is surrounded. The complex of physical, chemical, and biotic factors (as climate, soil, and living things) that act upon an organism or an ecological community and ultimately determine its form and survival. The aggregate of social and cultural conditions that influence the life of an individual or community [23]. State of being environed or surrounded c. 1600. Nature, conditions in which a person or thing lives, first recorded in 1827, used by Carlyle to render German *Umgebung*. Specialized ecology sense first recorded in 1956 [16].

2. *Malaria.* Italian. mal'aria, from *mala* = bad (feminine of *malo*, from Latin *malus*) + *aria* = air. Probably first used by Italian physician Francisco Torti in 1740 (1658–1741). The mosquito-borne disease was once thought to have been caused by foul air in marshy districts [16]. *American Trypanosomiasis.* An anthropozoonosis due to the protozoan *Trypanosoma cruzi* transmitted to man by infected faeces of a blood-sucking triatomine bug, skin break or through mucous membranes, contaminated food, blood transfusion, pregnancy and delivery, organ transplantation or laboratory accident endemic of Central and South Americas [24].

3. *Endemic.* *ενδημία* en in, within, inside + *demos* people or population and also place, home.

4. *Nidality.* Nidus according to Pavlovsky, a translation of Russian *ochag* = hearth, breeding ground. Thus a nidus of a disease is its nest, home, or habitat, equivalent to the Latin focus [25].

## References

[1] M. Sorre, "Complexes pathogènes et géographie médicale," *Annales de Géographie*, vol. 42, no. 235, pp. 1–18, 1993.

[2] E. N. Pavlovsky, *Natural Nidality of Transmissible Diseases with Special Reference to the Landscape Epidemiology of Zooanthroposes*, Edited by N. D. Levine, University of Illinois Press, 1966.

[3] E. N. Pavlovsky, *The Natural Nidality of Transmissible Disease*, Edited by N. D. Levine, University of Illinois Press, Urbana, Ill, USA, 1966.

[4] M. Le Bras and J. M. D. Malvi, "Le complexe pathogène tropical regard nouveau sur un concept ancien," *Médecine Tropicale*, vol. 64, pp. 613–618, 2004.

[5] M. G. Rosa-Freitas, P. Tsouris, A. T. Peterson et al., "An ecoregional classification for the state of Roraima, Brazil. The importance of landscape in malaria biology," *Memorias do Instituto Oswaldo Cruz*, vol. 102, no. 3, pp. 349–357, 2007.

[6] M. G. Rosa-Freitas, P. Tsouris, I. C. Reis, M. A. F. M. de Magalhães, T. F. S. do Nascimento, and N. A. Honório, "Dengue and land cover heterogeneity in Rio de Janeiro," *Oecologia Australis*, vol. 14, no. 3, pp. 641–667, 2010.

[7] M. G. Rosa-Freitas, P. Tsouris, J. F. Luitgards-Moura, A. T. Peterson, and N. A. Honório, "Ecoregiones de Roraima. A importância da paisagem na epidemiologia da malária," in *Homem, Ambiente e Ecologia no Estado de Roraima*, R. I. Barbosa, E. J. G. Ferreira, and E. G. Castellón, Eds., INPA, Manaus, Amazonas, Brazil, 2nd edition, 2010.

[8] W. Tobler, "A computer movie simulating urban growth in the Detroit region," *Economic Geography*, vol. 46, no. 2, pp. 234–240, 1970.

[9] R. S. Ostfeld, G. E. Glass, and F. Keesing, "Spatial epidemiology: an emerging (or re-emerging) discipline," *Trends in Ecology & Evolution*, vol. 20, pp. 328–336, 2005.

[10] J. Snow, "On the Mode of Communication of Cholera," 2nd edition, John Churchill, London, UK, 2012, http://www.ph.ucla.edu/epi/snow/snowbook.html.

[11] J. K. Berry, "Berry and Associates. Spatial Information Systems. Beyond Mapping III. Topic 27: GIS Evolution and Future Trends," 2009, http://www.innovativegis.com/basis/mapanalysis/Topic27/Topic27.htm#Contemporary.

[12] G. Câmara, A. M. Monteiro, S. D. Fucks, and M. S. Carvalho, "Spatial Analysis and GIS: A Primer. International Postgraduate Course on Geographic Information Technologies," Castellón, Spain, 2004, http://edugi.uji.es/Camara/spatial_analysis_primer.pdf.

[13] G. J. Upton and B. Fingelton, *Spatial Data Analysis by Example. Volume 1: Point Pattern and Quantitative Data*, John Wiley & Sons, New York, NY, USA, 1985.

[14] L. Anselin and S. Rey, Eds., *Perspectives on Spatial Data Analysis. Advances in Spatial Science*, Springer, Berlin, Germany, 2010.

[15] L. Anselin. "The future of spatial analysis in the social sciences," *Journal of Geographical Systems*, vol. 2, no. 1, pp. 11–15, 2000.

[16] Online Etymology Dictionary, 2012, http://www.etymonline.com/.
[17] Brainy Quote, 2012, http://www.brainyquote.com/words/la/landscape183500.html#ixzz1pAjNmYk0.

[18] R. T. T. Forman and M. Gordon, *Landscape Ecology*, John Wiley & Sons, New York, NY, USA, 1986.

[19] J. B. Dunning, B. J. Danielson, and H. R. Pulliam, "Ecological processes that affect populations in complex landscapes," *Oikos*, vol. 65, pp. 169-175, 1992.

[20] University of Massachusetts Landscape Ecology Laboratory, 2012, http://www.umass.edu/landeco/research/fragstats/documents/Conceptual%20Background/What%20is%20a%20Landscape/What%20is%20a%20Landscape.htm.

[21] W. D. Pattison, "The four traditions of geography," *Journal of Geography*, vol. 89, no. 5, pp. 202-206, 1990.

[22] Dictionary.com, 2012, http://dictionary/reference.com/browse/habitat.

[23] Merriam-Webster, 2012, http://www.merriam-webster.com/dictionary/environment.

[24] WHO, "Neglected diseases Chagas disease (American trypanosomiasis)," 2012, http://www.who.int/neglected_diseases/diseases/chagas/en/index.html.

[25] J. Caci, "Vector and Disease. Slides presentation," 2012, http://wairr-www.army.mil/Documents/TropMed/Feb-2012/(10)%20Caci_EntomologyTrop%20Med2012%20(NXPowerLite).pdf.
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