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Application of GIS technology in public health: successes
and challenges
STEPHANIE M. FLETCHER-LARTEY
1
* and GRAZIELLA CAPRARELLI
2
1
South Western Sydney Local Health District, Public Health Unit, PO Box 38, Liverpool, NSW 1871, Australia
2
Division of IT, Engineering and the Environment, University of South Australia, GPO Box 2471, Adelaide, SA 5001,
Australia
(Received 1 July 2015; revised 29 November 2015; accepted 14 December 2015)
SUMMARY
The uptake and acceptance of Geographic Information Systems (GIS) technology has increased since the early 1990s and
public health applications are rapidly expanding. In this paper, we summarize the common uses of GIS technology in the
public health sector, emphasizing applications related to mapping and understanding of parasitic diseases. We also present
some of the success stories, and discuss the challenges that still prevent a full scope application of GIS technology in the
public health context. Geographical analysis has allowed researchers to interlink health, population and environmental
data, thus enabling them to evaluate and quantify relationships between health-related variables and environmental risk
factors at different geographical scales. The ability to access, share and utilize satellite and remote-sensing data has
made possible even wider understanding of disease processes and of their links to the environment, an important consid-
eration in the study of parasitic diseases. For example, disease prevention and control strategies resulting from investiga-
tions conducted in a GIS environment have been applied in many areas, particularly in Africa. However, there remain
several challenges to a more widespread use of GIS technology, such as: limited access to GIS infrastructure, inadequate
technical and analytical skills, and uneven data availability. Opportunities exist for international collaboration to address
these limitations through knowledge sharing and governance.
Key words: Geographic information systems, infectious diseases, public health, parasitology, spatial analysis.
INTRODUCTION
Geographic Information Systems (GIS) play a
major role in health care, surveillance of infectious
diseases, and mapping and monitoring of the
spatial and temporal distributions of vectors of infec-
tion (Shaw, 2012). GIS combine sophisticated
algorithms, spatial analysis, geo-statistics and mod-
elling, making GIS technology a powerful tool for
the prediction of disease patterns and parasite
ecology associations (Higgs, 2004;Guoet al. 2005;
García-Rangel and Pettorelli, 2013). Given the
variety of tools, concepts and applications of GIS
in public health, a brief synthesis of the state of the
field is due. In this paper, we review examples of
successful applications of GIS in public health,
with emphasis on parasitic diseases. Some us eful
definitions and concepts of GIS discussed in this
paper are briefly introduced here, but we refer
the readers to Caprarelli and Fletcher (2014 and
references therein) for a comprehensive review of
GIS architecture, availability, analytical tools,
and for a synthesis of relevant principles of spatial
analysis and modelling (Caprarelli and Fletcher,
2014).
Every GIS is structured around five fundamental
components (Fig. 1): (i) spatially referenced data,
collected and stored in a relational geodatabase, i.e.
an information system from which data can be
retrieved by formulation of sequences of logical
queries; (ii) the hardware physically storing data
and processing tools; (iii) the software assembling
the user-interface algorithms by which users access
the database, query and analyse the data; (iv) the
algorithms and data management procedures; and
(v) the people, both producers and consumers of
spatial data. Each of these components incorporates
varying levels of complexity, depending on the
scope and scale for which GIS is used. Regardless
of the differences, all systems provide basic
mapping and spatial analysis tools, which can be
mastered in relatively short time even by users
with no programming skills. The most basic opera-
tions involve creating maps by overlaying data
stored as tables comprising details of geographic fea-
tures symbolized by points, lines or polygons, or
raster datasets (e.g., photographs), and their geo-
graphic coordinates (an example is shown in
Fig. 2). Once the features are mapped, geo-statistical
analysis, such as cluster analysis and network ana-
lysis, important for disease monitoring and invest i-
gation (Bergquist and Rinaldi, 2010), can be
carried out using the analysis tools included in the
GIS software package. This basic approach may be
followed by more complex modelling to understand
* Corresponding author. South Western Sydney Local
Health District, Public Health Unit, PO Box 38,
Liverpool, NSW 1871, Australia. E-mail: stephanie.
fletcher@sswahs.nsw.gov.au
1
Parasitology, Page 1 of 15. © Cambridge University Press 2016
doi:10.1017/S0031182015001869
the mechanisms of disease spreading, by linking
disease processes and explanatory spatial variables
(Graham et al. 2004). GIS has increased the accessi-
bility and reliability of integration between health
data and mapping processes (Brooker et al. 2009b),
allowing researchers to study the relationships
between spatial and temporal trends and risk
(Clements et al. 2006a; Brooker and Clements,
2009; Brooker et al. 2009b), and between environ-
mental factors and health, to all scales (McGeehin
et al. 2004; Beale et al. 2010). Examples include
the epidemiological application of data obtained
from climate-based forecast systems that include ob-
servation of oceans, land, elevat ions, land cover, land
use, surface temperatures and rainfall, for disease
surveillance and early-warning systems (Bergquist
and Rinaldi, 2010).
While GIS is broadly used in many countries as
part of routine public health management and ser-
vices, its diffusion is not uniform across developing
countries, where some of the most lethal and crip-
pling parasitic diseases are endemic. We will
discuss some of the challenges faced by many coun-
tries in adopting GIS for routine monitoring and
spatial analysis of infectious diseases and of the en-
vironmental factors contributing to their spreading.
We also suggest possible simple and low-cost initia-
tives that might assist embracing this technology
more widely where it is most needed.
RESEARCH METHODOLOGY
Personal experience, particularly of one of the
authors (SF-L), has been the principal source of in-
spiration for this paper. Working in the field in
several developing regions in Central America and
the Western Pacific, has brought about the realiza-
tion that the management of some of the most
debilitating infectious diseases require effective
approaches, informed by geospatial analyses at the
local level. While regulations and ethical con sidera-
tions do not allow dissemination of specific informa-
tion collected in the field, the authors believe that
addressing some of the more general aspects can
provide insight to public health practitioners glo-
bally. The author’s observations and experiences
derived from field notes [a concept frequently used
in qualitative studies (Baxter and Jack, 2008)],
have informed the challenges and solutions section.
These observations indicate that, in spite of the
obvious willingness of local public health workers
and some local communities to educate and train
themselves to combat and prevent the spread of
diseases, the uneven distribution of infrastructural
resources and expertise, otherwise taken for
granted in the developed world, stands in the way
of a systematic approach that would produce real
long-term benefits.
Peer-reviewed reports describing how GIS has
been successfully applied to the monitoring and pre-
diction of parasitic diseases, focusing mainly on
examples from developing countries, were also
reviewed. This provided a clear indication that the
information collected and managed within a GIS is
linked to real and measurable public health benefits
for communities in those regions. We then consid-
ered individually each of the five principal elements
composing a GIS (Fig 1), to identify possible bar-
riers to their effective deployment in developing
countries, and referred to examples in the peer-
reviewed literature that could highlight specific chal-
lenges. Following this step we then looked at
Fig. 1. The fi ve components of GIS represented as the edges of a pentagon (left polygon in the figure): data, hardware,
software, procedures, people. Public health data are perfectly suited to be treated and analysed as information layers in a
GIS, provided location specific information (e.g., geographic coordinates, addresses, street names, etc.) is also included in
the database. Additional information layers, for example census or environmental data, can be added in the database and
can be queried together with the health data in order to map, analyse, interpret and model the incidence and spread of
diseases. Where one or more of the GIS components are lacking or inadequately resourced, however, the geodatabase loses
its analytical and predictive power. Challenges (graphically represented by the right-hand pentagon in the figure) ensue
when data are collected unevenly or unrepresentatively, when the layers of information are handled by an unskilled
workforce, in the absence of proper data handling and storage procedures, and if adequate hardware and software cannot be
obtained. In dealing with zoonotic diseases, affecting mostly developing countries, these challenges represent obstacles in
building efficient and effective GIS architectures.
2S. M. Fletcher-Lartey and G. Caprarelli
possible solutions that could alleviate the challenges.
Where no examples of solutions were found in
the literature, we proposed some recommended
actions, informed by personal experiences. Our
literature search repositories were PubMed and
Google Scholar. Based on the study aim, we
adopted a discursive writing approach and a narra-
tive literature review approach rather than present-
ing a systematic review of the literat ure.
EXAMPLES OF SUCCESSES
Understanding the socio-cultural determinants of
health
Investments into disease prevention and control ac-
tivities should take into consideration the broad
socioeconomic, cultural, and educational determi-
nants, which are often modifiable predictors of
health outcomes (Njau et al. 2014). Epidemiological
studies have generally described the association
between various health determinants and the risk
for transmission and spread of infectious diseases.
Whilst it is generally accepted that some people are
more at risk of infection than others, some of the
underlying determinants of disease spread are not
clearly understood, and even within the same popu-
lation group, heterogeneity in disease distribution
exists, and has been identified through spatial ana-
lysis (Clements et al. 2013; Kasasa et al. 2013).
The application of GIS in disease studies has furth-
ered the understanding of the intersection between
person, place and time in infectious disease out-
breaks and underlying social and cultural factors.
These factors are often unevenly distributed but
the extent and intensity of a particular disease may
be influenced by their spatial distribution (Moore
and Carpenter, 1999). Epidemiological mapping
has helped to advance understanding of the social
and cultural perspectives of the spread of certain
Fig. 2. Draft map showing information layers in GIS. Example of draft of map prepared with information layers in GIS.
The location is the administrative province of Kasai Occidental, in the Democratic Republic of Congo. The map is
prepared by overlaying geo-referenced vector layers stacked on top of each other (as in the map legend) on a basemap
obtained from NASA satellite imagery through the free Bing Maps web mapping service (http://www.bing.com/maps/).
The vector layers were digitized by editing GIS shapefiles at a scale 1:3 322 505, using a WGS 84/Pseudo Mercator
projection, and taking the basemap as a reference map. Yellow circles (Cities): point shapefile; shaded red area
(Administrative unit): polygon shapefile; blue lines (Main rivers): polyline shapefile. The shapefiles and the map were
prepared using the open-source software QGIS v. 2·8·1 – Wien. No topological rules, validation procedure or error
corrections were applied, so the shapes and geographic coordinates of all vector layers in this figure must be considered
only approximations and the resulting map in the figure should not be used as a detailed geographical reference.
3Application of GIS in public health
infectious diseases. Social and cultural variables (such
as access to water, sanitation, health care, population
density, over-crowding, farming and nutritional
practices, to name a few) can be mapped in a
similar way to relevant environmental covariates
such as temperature and rainfall. Predictive model-
ling utilizes current estimates of disease burden to
predict future burden based on expected changes
in population demographics and relevant social
determinants (Lau et al. 2014). By understanding
the distribution of social determinants of health,
hotspots can be identified and targeted i nterven-
tions developed to address them (Schneider et al.
2011).
Poverty remains a significant social determinant in
the propagation of neg lected tropical diseases, and
forms part of a vicious cycle of reduced economic
productivity due to long-term disability and mor-
bidity, maternal and child health issues and other
health-related challenges. These limit productivity,
resulting in individuals and their communities
being caught in a health-related ‘poverty trap’
(Brooker et al. 2010; Conteh et al. 2010; Hotez and
Pecoul, 2010). The development of poverty maps
for many countries by the United Nations
Development Programme (UNDP), the World
Bank and similar agencies, has provided the means
by which health services can identify priority
populations.
Variability in human activities that may impact
inadvertently upon the life cycle of parasites and
their vectors and the degree to which humans are
exposed have improved understanding of emerging
and re-emerging diseases. This is possible when
local human activities (e.g., migration, outdoor
leisure activities and forest use) impact the nature
and level of contact between people, parasites
and/or their vectors (Semenza et al. 2010). The
value of geospatial databases has been demonstrated
through the incorporation of multiple sources of in-
formation on human health and demographics to de-
termine hotspots for disease transm ission, and the
use of predictive risk charts and maps to inform
public health interventions (Lau et al. 2014).
Mapping socio-economic and cultural deter-
minants has been successfully used to predict the
occurrence of parasite co-infections and multi-
parasitism (Raso et al. 2006). There is evidence
that the distribution of trachoma shows heterogen-
eity between districts and regions and while its oc-
currence has been linked to environmental
sanitation and behavioural factors, patterns at large
scales reflect disparities in socioeconomic status
and indicators such as water, sanitation and
hygiene (WASH) (Clements et al. 2010; Smith
et al. 2013). The spatial variation in the incidence
of WASH is associated with the geographical vari-
ation in soil-transmitted helminths (Smith et al.
2013). By understanding the spatial aspects of
WASH indicators, Magalhães et al.(2011) were
able to determine the contribution of water and sani-
tation to the overall burden of helminthic infections
in school-aged children. Geo-referenced househ old-
level data for three WASH indicators obtained from
demographic health surveys (DHS) conducted in
participating countries (Burkina Faso, 2003;
Ghana, 2003; Mali, 2006) were used to generate pre-
dictive maps of areas without piped water, toilet fa-
cilities and improved household floor types. This
facilitated the identification of areas in West Africa
that were lagging behind the Millennium
Development Goals for water and sanitation
(Magalhães et al. 2011). The authors were then
able to quantify the role of WASH in the risk of
Schistosoma hematobium, Schistosoma mansoni and
hookworm infection in school-aged children. Lack
of access to clean water and sanitation is a possible
determinant of polyparasitism (Brooker and
Clements, 2009). The ability to identify this associ-
ation between WASH and the occurrence of parasit-
ic diseases facilitates identification of communities
in West Africa where interventions to prevent
disease spread and improvement of WASH can
produce greater health benefits (Magalhães et al.
2011). Similarly for Western Côte d’ Ivoire, demo-
graphic, environmental, and socioeconomic data
were incorporated into GIS in order to conduct
risk profiling and spatial prediction of co-infection
with Schistosoma mansoni and hookworm. The evi-
dence suggests that the socioeconomic status was
useful in predicting co-infections between S.
mansoni and hookworm at small geographical scales
(Raso et al. 2006). Maternal education mapped at
small scale was found to be a significant variable
associated with the availability of water supply and
sanitation facilities in households in West Africa.
Higher levels of maternal education were correlated
with childhood protection from helminth infection
(Magalhães et al. 2011).
Household and community practices have been
mapped to understand disease risk in the Pacific
Island Countries. In Papua New Guinea, spatial
stratification of district-specific risks associated with
high-risk areas of malnutrition was used to describe
the spatial features associated with the prevalence of
stunting and wasting outcomes at the province and
district-levels (Wand et al. 2012). By conducting a
spatial analysis study, high geographical variability
of stunting and wasting over the targeted region
was identified. This was useful to highlight dis-
trict-level differences in health outcomes, which are
often masked because of data aggregation, resulting
in misleading conclusions (Wand et al. 2012). The
advancement in understanding of the distribution
of social determinants of health will continue to
inform ongoing targeted surveillance and the devel-
opment of interventions to prevent and control
infectious diseases (Schneider et al. 2011).
4S. M. Fletcher-Lartey and G. Caprarelli
Disease surveillance and early warning systems
Public health surveillance is defined as ‘the continu-
ous, systematic collection, analysis and interpretation
of health-related data needed for the plann ing, imple-
mentation, and evaluation of public health practice’
(World Health Organization, 2015). Effective sur-
veillance systems provide early warning systems for
public health emergencies, assess the impact of inter-
ventions or evaluate progress towards specified
goals, and monitor trends in the development and
proliferation of health threats, informing the priori-
tization of issues, allocation of resources, public
health policy and strategies. Mapping with GIS
tools is increasingly being used globally as part of
disease surveillance and monitoring programmes.
Maps provide a symbolic representation of under-
lying geographical distribution of disease incidence,
improving the understanding of disease rates over
time, and enabling the detection of outbreaks or pos-
sible epidemics (Bailey, 2001; Norstrom, 2001;
Boulos, 2004; Blanton et al. 2006; Duncombe et al.
2012; Kelly et al. 2013). Spatial observations of en-
vironmental factors such as rainfall, land use,
surface temperatures, oceans and land cover have a
direct epidemiological impact on the transmission
of diseases. Consequently, the ability to apply GIS
techniques to disease surveillance has opened up a
world of possibilities in creating early-warning
systems for emerging and re-emerging diseases
(Bergquist and Rinaldi, 2010).
Geospatial tools improve understanding of the
spatiotemporal distribution of parasitic diseases
and thus enhance our ability to design appropriate
cost-effective integrated disease control programmes
(Brooker and Utzinger, 2007; Fletcher et al. 2014).
For example, geospatial tools assisted Jamaica to
rapidly control and eliminate malaria after its re-
introduction to the country in 2006. Mapping
revealed the foci of infection and enabled targeted
intervention and rapid containment of the outbreak.
Public health officials were able to divide the affected
area into 23 geographic grids, eight of which corre-
sponded to the affected communities. This enabled
surveillance teams to systematically examine com-
munities for anopheles breeding sites that were sub-
jected to larvicidal treatment or implementation of
environmental controls, and concentration of adulti-
cidal treatment in the affected grids (Webster-Kerr
et al. 2011).
The incorporation of GIS technology into routine
disease surveillance has been achieved in some re-
source limited settings based on increased recogni-
tion of the value of GIS technology in the
understanding and control of infectious diseases,
which has led to increased political, financial and
technical support for such programmes (Malone
et al. 2001; Zhou et al. 2009; Brooker et al. 2009b).
The incorporation of a GIS-based spatial decision
support system (SDSS) into the surveillance-
response system in the South Pacific is a major
achievement for Vanuatu and Solomon Islands.
The SDSS is designed to automatically locate and
map confirmed malaria cases, to classify active foci
of infection, and to guide targeted interventions.
With technical assistance provided by the Pacific
Malaria Initiative Support Centre (PacMISC) and
WHO, local authori ties were able to build custom
applications into the existing provincial SDSS
used in previously identified elimination provinces,
to support general topographic mapping, geographic
reconnaissance and vector control intervention
management. This enabled teams to automatically
classify and map transmission foci based on the
spatiotemporal distribution of cases, and to identify
priority areas of interest for the implementation of
foci-specific targeted response (Kelly et al. 2013).
Several developing countries now have to access
GIS technology as a result of their participation in
the Global Fund for Tuberculosis, Malaria and
AIDS programmes (Chang et al. 2009). The
Global Fund for Tuberculosis, Malaria and AIDS
programmes is a partnership between governments,
civil society, the private sector and people affected by
these diseases to accelerate the end of AIDS, TB and
malaria as epidemics (http://www.theglobalfund.
org/en/).
Other examples demonstrate that the incorpor-
ation of GIS technology into disease surveillance
systems has facilitated the control of infectious
diseases such as: cutaneous leishmaniasis (Ali-
Akbarpour et al. 2012), human African trypano-
somiasis (Cecchi et al. 2009; Simarro et al. 2010),
schistosomiasis (Bergquist, 2002; Raso et al. 2005;
Clements et al. 2006a; Brooker, 2007; Ekpo et al.
2008), loiasis (Diggle et al. 2007), various animal dis-
eases (Norstrom, 2001; Clements et al. 2007), tick-
borne diseases (Randolph and Rogers, 2006;
Estrada-Peña, 2007), rabies (Blanton et al. 2006)
and malaria (Keating et al. 2003; Snow et al. 2005;
Gosoniu et al. 2006; Mabaso
et al. 2006;Hayet al.
2009; Grillet et al. 2010; Mboera et al. 2011; Noor
et al. 2012).
A vector-borne disease surveillance system was
established in American Samoa to monitor the elim-
ination progress of lymphatic filariasis after mass
drug administration (MDA) from 2000 to 2006.
Spatial epidemiology was incorporated into the
system and applied to geo-referenced serum bank
data to look for hot spots of transmission of lymph-
atic fi lariasis based on spatial dependence, and
household level clustering based on the assessment
of the seroprevalence of lymphatic filariasis antigens
and antibodies (2010) in American Samoan adults.
Geographic analysis identified the possible location
and estimated size of residual foci of potentially in-
fectious adults. The study demonstrated the value
of spatial analysis in post-MDA surveillance and
5Application of GIS in public health
confirmed the risk of re-introduction of the disease
by new migrants, while identifying strategies to de-
termine whether ongoing targeted surveillance of
high risk groups was warranted (Lau et al. 2014).
The rapid epidemiological mapping of onchocer-
ciasis (REMO) in over 20 African countries
(Brooker et al. 2010) led by the African
Programme for Onchocerciasis Control (APOC),
quickly and cheaply identified priority areas and
the number of individuals requiring treatment by
community-directed treatment with ivermectin
(CDTI) (Noma et al. 2002; Brooker et al. 2010).
This was achieved due to the ability to conduct
rapid assessments that enabled the stratification of
countries into areas that are suitable and unsuitable
for transmission (Brooker et al. 2010).
Increased availability and access to geospatial tools
facilitates the acquisition of advantageous geograph-
ical and environmental perspectives on the diseases
(Beale et al. 2010). Increased availability of free or
inexpensive tools (such as WHO’s HealthMapper
or CDC’s EpiMap) for mapping disease distribution
and community treatment information has enabled
public health workers to be more effective and
reach wider population. Recognizing the burden
from neglected parasitic diseases upon affected coun-
tries, the World Health Assembly resolved that the
elimination of lymphatic filariasis and onchocerciasis
was a public health priority for the WHO and its
member-states (World Health Assembly, 1997).
Remarkable progress has been made towards the
elimination of targets due to the ability to map the
distribution of disease, and conduct spatial analysis
to evaluate transmission levels in populations
under MDA (Molyneux, 2003). Geographical ana-
lysis was cond ucted to determine the level of risk
of infection amongst populations residing in
‘Implementation Units’ (or health districts) in pro-
gramme countries (Hooper et al. 2014). Mapping
of the geographical distribution of infected persons
and spatial modelling to determine the magnitude
of the population needing intervention were critical
to the progress of elimination efforts (Ottesen,
2000; Hooper et al. 2014).
Luan and Law (2014) provided an in-depth
review of web GIS-based Public Health
Surveillance Systems (WGPHSSs). One note-
worthy example of how GIS has been applied to
other infect ious diseases is the World Health
Organization (WHO)’s DengueNet, a centralized
data management system that includes a database
and GIS for the global epidemiological and viro-
logical surveillance of dengue fever (DF) and
dengue haemorrhagic fever (DHF). This web-
based system makes available to users a standard
platform where current surveillance data on the inci-
dence and trends of dengue and DHF are shared.
Data are standardized and reported at the country
level resulting in greater comparability of the reported
cases of dengue fever across di
fferent geographical
areas. This translates into useful early warning infor-
mation for public health professionals who can then
be better prepared for the management of individual
cases and epidemics, thus reducing fatality rates.
The data can also be used to relate health and eco-
nomic conditions to the cost effectiveness of preven-
tion and control interventions (World Health
Organization, 2014), thus building useful blueprints
for prediction and prevention of future outbreaks.
Growth in the user-base of GIS technology
applied to dengue fever has improved our under-
standing of the geographical prevalence of the
disease, of its distribution over time, hence its
spreading potential, and has enabled the evaluation
of the spatial relationships between incidence and
disease risk factors to inform effective control pro-
grammes (Duncombe et al. 2012;Hsuet al. 2012;
Luan and Law, 2014). There are several other exam-
ples where the successful application of inexpensive
geodatabase tools have resulted in long-term benefits
for communities: the utilization of epidemiological
data, rapid assessment surveys and climate-based
risk prediction models to map the distribution of
urinary and intestinal schistosomiasis across Africa
(Brooker et al. 2009a); the development of empirical
databases and predictive maps which describe the
global distribution of helminths (Brooker et al.
2000; Brooker, 2010; Brooker et al. 2010) and
malaria (Snow et al. 2005; Hay and Snow, 2006);
the use and analysis of raster datasets obtained
from orbit using remote sensing techniques, in
order to map the distribution of sch istosomiasis
and a variety of other parasites, and to study the as-
sociation between infection and environmental vari-
ables (Cross and Bailey, 1984; Cross et al. 1984;
Malone et al. 2001; Brooker et al. 2002; Clements
et al. 2006a, b).
International collaboration on zoonotic parasite
diseases
There is growing political and financial commitment
in both developed and developing countries to estab-
lish measures aimed at providing efficient and cost-
effective control of neglected tropical diseases.
These include infections mostly endemic to low-
income populations in Africa and the Middle East,
South America and Asia, (Zhou et al. 2009;
Simarro et al. 2010; Scholte
et al. 2012). In recogni-
tion of the need to improve understanding of the
social, economic and environmental burden caused
by these diseases, global experts have come together
to support the establishment of spatial databases
aimed at profiling multiple species of parasitic dis-
eases (Malone et al. 2001; Zhou et al. 2009 ). One
example of a spatial database of parasitic diseases is
the Global Network for Geospatial Health (GNGH),
established in 2000, initially set up to develop
6S. M. Fletcher-Lartey and G. Caprarelli
computer-based models to improve control pro-
grammes for schistosomiasis and other snail-borne
diseases of medical and veterinary importance
(Malone et al. 2001; Zhou et al. 2009). The scope
of the GNGH has since expanded to include other
widespread infectious diseases, such as soil-trans-
mitted and waterborne helminth infections, as well
as arthropod-borne diseases such as leishmaniasis,
malaria and lymphatic filariasis (Zhou et al. 2009).
In the Latin American and Caribbean Region,
The Pan American Health Organization developed
a Regional Strategic Framework to address
neglected diseases (NDs) in neglected populations.
The aim of the plan is to strengthen surveillance,
prevention, and control system s for neglected dis-
eases, and by extension strengthen other disease sur-
veillance and control programmes. The plan outlines
that epidemiological surveillance and mapping are
integrated into the achievement of three strategic
priorities: (1) diseases that can be eliminated by
mass preventive or targeted chemotherapy alone;
(2) diseases that can be controlled by mass prevent-
ive or targeted chemotherapy coupled with inten-
sified, improved, early case detection and
management; and (3) diseases which require
improved transmission control through better
health promotion, behaviour change, emergency
preparedness, and environmental sanitation and
management strategies (Ault, 2007). The expected
outcome of the strategic framework is to improve
public health by developing multi-disease based sur-
veillance systems and incorporating GIS into the
planning, monitoring, risk and impact assessment
processes to inform decision-making for NDs in
the participating countries and communities (Ault,
2007).
WHO and the Partners for Parasite Control are
coordinating a global programme to control hel-
minths and schistosomiasis. The partnership,
formed in 2001, includes governments of WHO
Member States where helminthic infections are
endemic, governments of Member States committed
to reduce poverty in low-income countries, various
United Nations (UN) agencies, universities, philan-
thropic foundations and pharmaceutical companies
(Ault, 2007). The aim of the partnership is to
deliver permanent relief from helminthic diseases
for millions of affected people by utilizing risk
mapping, regular chemotherapy, and education in
the control of at least 75% of all school-age children
at risk of morbidity from schistosomiasis and soil-
transmitted helminthiasis (World Health
Organization, 2005; Ault, 2007).
The First International Symposium on Geospatial
Health was organized by the GNGH in Lijiang,
Yunnan province, People’s Republic of China in
September 2007. The aim of the symposium was
‘to review advances made in the control of zoonotic
parasitic diseases through the use of geospatial tools’.
The symposium brought together local and inter-
national scientists to encourage sharing of data and
geospatial health applications in formats that can
be used across health disciplines in different contexts
(Zhou et al. 2009 ).
Since then, other collaborative approaches have
emerged targeting parasitic disease control. The
Roll Back Malaria (RBM) Partnership has developed
the Global Malaria Action Plan (GMAP) (http://
www.rollbackmalaria.org/microsites/gmap/default.
html). The GMAP is based on input from experts
from 30 malaria endemic countries and regions, 65
international institutions and 250 experts globally,
consolidated into a vision for a substantial and sus-
tained reduction in the burden of malaria and the
eventual global eradication of malaria. The GMAP
provides a global framework for action to assist part-
ners to coordinate their efforts through an evidence-
based approach for the delivery of e
ffective preven-
tion and treatment to all people at risk, and estimates
the annual funding needed to achieve its goals (Roll
Back Malaria Partnership, 2008).
Several other international projects involve the
use of datasets on a global scale. The Malaria
Atlas Project (MAP; http://www.map.ox.ac.uk/)
managed by the University of Oxford, in the
United Kingdom (UK), brings together researchers
interested in developing techniques to map and
understand the distribution and spread of malaria.
The project seeks to support effective planning of
malaria control (Hay and Snow, 2006). The online
portal provides access to maps and data processing
tools that are updated regularly to ensure the infor-
mation provided stays current. Users with advanced
mathematical and geospatial skills may also down-
load programs and instructions for data modelling
and apply the spatial analysis concepts to new
datasets. Publications by scientists contributing to
the project are also accessible from the website.
The London School of Hygiene and Tropical
Medicine in the UK manages the Glob al Atlas of
Helminth Infections (GAHI; http://www.this
wormyworld.org/). The GAHI uses data from thou-
sands of field surveys to provide reliable and updated
maps of helminth infection distribution to facilitate
and prioritize targeted treatment. A variety of
maps including survey data maps, predictive risk
maps and control and planning maps can be accessed
through the GAHI and new datasets can be for-
warded to the project team members directly by
email. The project team provides capacity building
in mapping and the use of epidemiological tools.
GAHI also provides access to updated publications
(Brooker et al. 2010).
An example of how regional organizations have
attempted to build local mapping and analytic
capacity through technical cooperation is with
SIGEpi: ‘Application and development of GIS in
Epidemiology and Public Health.’ The purpose of
7Application of GIS in public health
this project is to strengthen the analytical capacities
of the Ministries of Heal th and other institutions
of the Pan American Health Organization (PAHO)
WHO member countries in the ‘Region of the
Americas’ and other regions. The SIGEpi’s license
is available to local ministries and institutions upon
request through the PAHO/WHO Representative
Offices in the respective countries, and to other pro-
fessionals, academics, researchers and the private
sector directly from the Management of the Area
of Analysis of Health and Information Systems
(AIS) of PAHO (Martínez Piedra et al. 2001).
There are opportunities to learn from the many
examples of successful studies combining GIS, sat-
ellite data and spatial epidemiology concepts, to
enable application to other infectious vector-borne
diseases (Yang et al. 2005; Brooker, 2007; Ekpo
et al. 2008; Simoonga et al. 2009; Bergquist and
Rinaldi, 2010; Brooker et al. 2010; Zeng et al.
2011). The query and analysis tools developed in a
GIS framework would assist with decisions regard-
ing the most effective deployment of defence mea-
sures against all vector-bo rne diseases. There is
much scope for wider application of GIS in Latin
America, the Caribbean and the Asia Pacific
region, where internet access is constantly improv-
ing. These regions share similarities in geographical
and ecological risk factors for infectious diseases and
as such there is scope to translate some of the experi-
ence acquired in Africa to these regions.
CHALLENGES AND SOLUTIONS
The scale at which mapping and geostatistical ana-
lyses are carried out is extremely important if GIS
is to be adopted as an effective tool in the hand of
communities and public health officials to control
the spread of parasitic diseases. Data collected from
the Global Infectious Diseases and Epidemiology
Network (GIDEON), complemented by those
mined from indexed PubMed publications, were
analysed by Hay et al.(2013) to obtain a global per-
spective regarding the extent by which infectious
diseases are mapped. These authors found that
only 7, out of 174 clinically significant mappable in-
fectious diseases, had actually been documented for
their spatial distribution. Their list however
missed some of the most diffused, chronic and de-
bilitating tropical parasitic diseases, such as trach-
oma and soil-transmitted helminths, because
distribution of these parasitic diseases and of the
relevant agents and vectors of infection did not
meet the first inclusion criterion of being spatially
variable at the global scale at which the statistical
analysis was conducted, and was thus considered a
low priority for mapping (Hay et al. 2013). Some
important infectious diseases show no spatial vari-
ability at global planetary scale, but prove to be
space dependent when their prevalence is analysed
at finer statistical scale and interpreted against envir-
onmental and ecological data.
In commending the massive statistical work
undertaken by these authors, Smith et al.(2013)
pointed out however that, while Hay et al. ’ s (2013)
analysis provided a valuable framework for global-
scale interpretations, mining data only from the
GIDEON database and from topical publications
indexed in PubMed, did not capture important
local data collection and open-source mapping
initiatives feeding into the regional scale modelling
on which surveillance, prevention and eff ective
intervention initiatives must ultimately be based
(Smith et al. 2013). Additionally, local and regional
mapping efforts (where available), supported by
open-access projects (such as those listed in the pre-
vious sections of this review) demonstrate granular-
ity at the local and regional scale (Clements et al.
2010; Magalhães et al. 2011). Furthermore, local en-
vironmental conditions, and the ecological dimen-
sion of parasitic spread, must be considered an
integral par t of the spatial analysis of infectious
disease transmission, requiring complex high-
resolution modelling of several types of data layers
(e.g., Caprarelli and Fletcher, 2014) over a range of
scales. Reliable spatial information gathered locally
by public health officers, health carers, environmen-
tal assessors and the general community must there-
fore be included in all spatial analyses and models of
infectious diseases. However, this information is
often lacking.
Some deadly diseases, such as dengue fever, to
date have received very little attention (Eisen and
Lozano-Fuentes, 2009). This may be in part
because of the uneven distribution of resources in
affected countries and the lack of uniformity in
data collection processes (Brooker et al. 2009a;
Brooker
et al. 2010). For example, while popula-
tion-level datasets on the incidence of human infec-
tion are generally available for Burundi, Rwanda and
Uganda, data from Kenya and Tanzania are sparse
and statistically non-representative (Brooker et al.
2009b; Brooker et al. 2010). This has limited the ap-
plication and impact of geospatial mapping efforts.
There is consistent evidence that the application of
GIS technology to public health and parasitology
has far reaching benefits, particularly to study the
distribution of parasites and their vectors. Most of
the focus has been on the African continent, which
suffers significant burden from infectious and
neglected parasitic diseases. The limited application
of GIS in other regions suggests there may be some
challenges to its widespread uptake and application.
In the following sections we list and examine some
of these challenges and suggest cost-effective solu-
tions. Content is based on the available literature,
complemented and supplemented by the personal
experiences of one of the authors (SF-L) gained
over many years working in various capacities in
8S. M. Fletcher-Lartey and G. Caprarelli
Public Health in different geographical settings.
Table 1 summarizes some of the major challenges
and proposed cost-effective solutions, particularly
geared at low- and middle-income settings.
Limited access to GIS infrastructure
Lack of infrastructure has historically been a barrier
to the utilization of GIS technology. This is partly
related to the need for sophisticated (and usually ex-
pensive) licensed GIS software, which may be a
significant hurdle for resource limited settings
(Bergquist and Rinaldi, 2010). Significant costs are
associated with the development of SDSSs, which
often require specialized equipment. One study
found that geographical reconnaissance accounted
for the majority of the costs and, had household
geo-reference data been previously collected, the
costs would have been significantly reduced
(Marston et al. 2014).
There is increasing use of GIS in the mapping of
households in countries where the technology is
readily available (Clements et al. 2013;Kellyet al.
2013). Open source GIS software is becoming in-
creasingly user friendly, by incorporating graphic
user interfaces (GUI) in addition to traditional
command line operations, and a variety of algorithms
and structured query language (SQL) packages analo-
gous to those of the commercial options (Table 1 in
Caprarelli and Fletcher, 2014). The use of low-cost
internet and free GIS infrastructure is documented
by Fisher and Myers (2011). Their experience
demonstrates how free software can be effectively
applied to mapping and preliminary geospatial ana-
lysis, without the need for any centralized database
or internet access (Fisher and Myers, 2011).
Chang et al. (2009) have successfully developed a
low-cost mapping and geo-referencing system
which does not rely on continuous access to
Internet, and is particularly useful for vector-borne
disease surveillance and control. The system,
created in Nicaragua as part of a nation-wide initia-
tive, was successfully built around satellite images
from Google Earth 4·3 (Google Inc. Mountain
View, CA, USA) and constructed with ArcGIS 9
ArcMap software (ESRI, Redlands, CA, USA)
made available through the Global Fund Program.
The authors were able to easily manipulate base
maps using ArcGIS and Erdas Imagine software,
enabling future users to work with the complete sat-
ellite map without need of an Internet connection.
The system is flexible and scalable, and could
easily be replicated in other developing contexts
with limited internet access (Chang et al. 2009),
using open source GIS software.
Limited technical capacity and experience
There are several analytical techniques employed in
the application of GIS technology which require
from basic to more advanced skills (see Caprarelli
and Fletcher, 2014 ). However, many organizations
still do not have access to even the basic technical ex-
pertise, properly trained or devoted staff, to focus on
GIS-related activities and to follow standardized
procedures (Boulos, 2004). Many humanitarian
and development focused agencies are increasingly
utilizing GIS as par t of their work in developing
countries but lack of local technical capacity has
resulted in external technical experts having to be
brought in to build capacity for GIS-related
activities (Kaiser
et al. 2003 ; Eisen and Lozano-
Fuentes, 2009). This increases the cost of the tech-
nology to the local organizations. Building local
capacity is necessary for sustained use and mainten-
ance of the resources, and requires a concerted effort
to empower individuals and group s, sufficient time
for training, and motivation and strategic planning.
All these factors must exist to ensure that knowledge
transfer, up-skilling and building of local technical
capacity actually occurs (Ramasubramanian, 1999).
International aid or donor-driven programmes in
aid-dependent economies often find limited local
capacity on which to build, due to limited or
already stretched human resources, lack of institu-
tional will and numerous competing priorities, un-
sustainable practices, heavy reliance upo n technical
assistance, with little or no transfer of technical
skills, thus undermining post-project sustainability
(Kimaro and Nhampossa, 2007; Chapman, 2010).
Hence, the vicious cycle of dependence on external
technical assistance in developing capacity continues
(Godfrey et al. 2002; Eade, 2007).
One solution to this problem could be for local au-
thorities to ensure that a capacity-building compo-
nent that facilitates technical up-skilling of local
personnel is included in all technical agreements.
Strategic planning for workforce needs should also
include identifi cation of training capacity, both in
terms of those to be trained and the source of train-
ing, and where this is not available locally, a suitable
sustainable alternative ought to be identified. This
may require strong links and collaboration with aca-
demic institutions and industry partners. Many
high-income countries now offer scholarships/fel-
lowships to developing counterparts for technical
up-skilling, and GIS should be an area included in
this development agenda. Local scientists could be
regarded as assets in this area, by being involved in
developing local GIS capability using their under-
standing of the local context, politics and needs, to
ensure sustainability through continued knowledge
transfer and skill development of local public
health officers (Dunn et al. 1997; Sieber, 2000;
Saikia, 2010), perhaps in the form of targeted high-
intensity short training courses.
The experience with SIGEpi demonstrates how
development partners have collaborated to build
local capacity through technical cooperation.
9Application of GIS in public health
Table 1: Summary of challenges and possible solutions to the application of GIS in public health settings.
Themes Challenges and solutions
1. Access to GIS infrastructure: 1.1. Lack of infrastructure and of sophisticated costly GIS software (Bergquist and
Rinaldi, 2010)
Solutions: Open source GIS software with user-friendly GUI, algorithms, and structured query
language (SQL) packages analogous to those of the commercial options (Table 1,
Caprarelli and Fletcher, 2014)
1.2. Costs associated with the development of spatial decision support systems re-
quiring specialized equipment, and cost of geographical reconnaissance
(Marston et al. 2014)
Solutions: Free software including functionality not requiring centralised databases or internet
access (Chang et al. 2009). Examples include: Cybertracker – for field data collection
on GPS-enabled PDAs (personal digital assistant); Open Jump http://www.open-
jump.org/ , a Java-based, open source GIS – for data visualisation and simple ana-
lysis; and AccessMod
©
http://www.who.int/kms/initiatives/accessmod/en/, a free
extension from the World Health Organisation (WHO), used for service availability
mapping (Fisher and Myers, 2011)
1.3. Convoluted structured procedures and hidden costs associated with different
levels of licensing and usage access of free software and data resources from
mapping system providers (for e.g. SIGEpi)
Establishment of scaled down process for emergency situations to enable mapping
resources to reach a broader pool of lower level users, thus facilitating fast and
topical analyses and response strategies
2. Technical capacity and
experience
2.1. Limited or no access to properly trained staff capable of focusing on GIS related
activities and to follow standardised procedures (Boulos, 2004)
Solutions: Building local capacity aimed at sustained use and maintenance of GIS resources,
including sufficient time for training, and motivation and strategic planning
(Ramasubramanian, 1999)
2.2. Limited local capacity due to limited or already stretched human resources, lack of
institutional will and numerous competing priorities, unsustainable mapping
practices (Clarke et al. 1996;Kaiseret al. 2003;McLafferty, 2003;Kimaroand
Nhampossa, 2007; Eisen and Lozano-Fuentes, 2009; Chapman, 2010)
Solutions: (a) Strategic planning for workforce to include identification of training capacity
(personnel to be trained and the source of training) and, where this is not locally
available, identification of a suitable sustainable alternative
(b) Establishment of long term links and collaboration with academic institutions
and industry partners
2.3. Heavy reliance on technical assistance, with little or no transfer of technical skills,
undermining post-project sustainability (Godfrey et al. 2002; Eade, 2007)
Solutions: (a) Capacity building component aimed at technical up-skilling of local personnel
to be included in donor funded technical agreements
(b) Local experts (scientists, engineers, academics) to be engaged in framing long
term sustainable solutions for development and scalability of local GIS cap-
ability by knowledge transfer and up-skilling of local staff (Dunn et al. 1997;
Sieber, 2000; Saikia, 2010)
3. Data availability and
analysis capacity
3.1. Limited availability of good quality spatial data; privacy and confidentiality
issues; restrictions to access and use of individual health incidence and outcomes
data; data ownership; ability to link or cross-reference publicly available data
due to inconsistencies in data collection parameters and systems (McLafferty,
2003; McGeehin et al. 2004; Beale et al. 2010)
Solutions: (a) Regular updating of data, allowing the addition of environmental and geographical
variables to historical datasets that previously lacked them (Brooker et al. 2009b;
Brooker et al. 2010)
(b) Inclusion of data sharing protocols to existing or new international collaborative
approaches
3.2. Lack of uniformity in the way disease-related metrics (rates, incidence, preva-
lence) are recorded and reported within and between countries, and inconsist-
encies in the use of a wide array of covariates, complicating the development of
national, regional and globally comparative maps of the same diseases (Hay
et al. 2013). Imprecise exposure measurements based on proxy variables which
can result in underestimating true effects, or lead to regression dilution bias
(Frost and Thompson, 2000; Magalhães et al. 2011)
(a) Encourage sharing of data collection tools and establish standardised format for
data collection and storage in global repositories
(b) Facilitate sharing of data through existing international collaborative approaches
to make data available and accessible ensuring global public health is protected.
For example, sharing disease surveillance data for The Outbreaks Global Incident
Maps, that display outbreaks, cases and deaths globally, caused by viral and
bacterial diseases, has potential to indicate biological terrorism threats (Global
Incident Map, 2012)
10S. M. Fletcher-Lartey and G. Caprarelli
However, the process of requesting resources,
support and engagement from the consortium
members i s highly structured, requiring a detailed
proposal and high level institutional i nvolvement.
This may limit the effectiveness of this system to
completely fulfil its stated objective to be a ‘GIS
tool for different analytical procedures and pro-
cesses related to monitoring of health events,
health situation analysis, and support for the deci-
sion-making in health’ (Pan American Health
Organization, 2008) in the broader Central and
South American region. This is ultimately a
problem, considering that the spread of infectious
diseases does not recognize human-made political
barriers between countries. Hence, affordability
by the largest possible number of local and regional
institutions should be one of the priorities to imple-
ment, and economic studies should provide ancil-
lary data to envisage sustainable strategies to make
this possible.
Limited data availability and analysis capacity
While the use and recognition of geographical infor-
mation systems in health care and research institu-
tions is increasing, some authors have lamented the
fact that health and population datasets are imported
on an ad-hoc basis and as such are not routinely
stored or available for analysis (McGeehin et al.
2004; Beale et al. 2010). This generates problems,
both in relation to the statistical uncertainties that
are introduced as a consequence of uneven distribu-
tion of data in the spatial analysis, and in relation to
the effectiveness of intervention, considering that the
most data-poor areas are generally those that would
require the most intensive monitoring (e.g., low-
and middle-income countries, or less wealthy area
codes in developed countries). Thus, in the public
health context, an essential consideration for the
use of GIS applications is the availability of good
quality data, with access and utilization guided by
appropriate polic ies and standard operating proce-
dures, to ensure that public health policy and prac-
tice are informed by the best available evidence
(Boulos, 2004).
Problems often arise with the use of population-
based surveys that were not collected for mapping.
This can lead to the use of clustering of data
resulting in uneven geographical coverage (Clements
et al. 2010), and the use of proxy variables which are
imprecise exposure measurements, resulting in
underestimation of the true effects or in a regression di-
lution bias (Frost and Thompson, 2000;Magalhães
et al. 2011). Lack of uniformity in the way disease-
related metrics (rates, incidence and prevalence) are
recorded and reported within and between countries,
and an inconsistency in the use of a wide array of
covariates, complicates the development of national,
regional and globally comparative maps of the same
diseases (Hay et al. 2013).
Good quality data are needed to determine the
effectiveness of different spatial models such as
Table 1: (Cont.)
Themes Challenges and solutions
3.3. Lack of appropriate policies informed by the best available evidence and
standard operating procedures to guide access and utilisation of public health
data
Solutions: (a) Development of data governance procedures and ethical processes ensuring
streamlined de-identification, storage and access to data
(b) Development and establishment of policies and standard operating procedures
to guide data access and utilisation based on the best available evidence (Boulos,
2004)
3.4. Restricted access to data obtained via sponsors, donors or grants
Solutions: (a) Agreements between granting donors, sponsors and users must be set in place to
govern future ownership of the data collected. Such agreements must also
include instructions for managing the infrastructure, and provisions for post-
grant integration and management of infrastructure into local systems
(b) Donors and local partners need to establish clear standard operating procedures
about the data entry (who, what, and when) and the sharing of information
between stakeholders at the local level (Dunn et al. 1997; Sieber, 2000; Saikia,
2010). Some international organisations have already produced open access
declarations in support of publicly funded research been made publicly avail-
able as a global public good (Chan et al. 2005)
3.5. Lack of uniform approach in quantifying the level of heterogeneity required for
intervention effectiveness (Clements et al. 2013; Kasasa et al. 2013)
Solutions: (a) Setting-up a formal framework to assess the effect of spatial decision support
systems on disease elimination, and support of research aimed to identify
measureable indicators for assessing appropriateness and effectiveness of geo-
spatial methods (Clements et al. 2013)
(b) Operational research and randomised controlled trials to be carried out in order
to determine the effectiveness of geospatial methods in real world settings
11Application of GIS in public health
spatially targeted vs. ad hoc or spatially uniform re-
source allocation strategies for disease elimination
(Clements et al. 2013). Geospatial methods can be
applied to the identification of malaria hotspots by
investigation of spatial heterogeneity at different
scales (Clements et al. 2013; Kasasa et al. 2013).
Limited emphasis has been placed on the conduct
of operational research and randomized controlled
trials that can determine the effectiv eness of geospa-
tial methods in real-world settings. Clements et al.
(2013) suggested that these types of studies are
needed to demonstrate the effectiveness of geospatial
science on improving decision-making and resource
allocation in real-world elimination programmes.
They postulated that a formal framework is needed
to assess the effect of SDSSs on malaria elimination
and that research is needed to identify measureable
indicators to assess geospatial methods (Clements
et al. 2013).
With the availability of cheaper and more user-
friendly GIS technology, some of the problems of
uneven data capture are being resolved: regular up-
dating of data, allowing the addition of environmen-
tal and geographical variables to historical datasets
that previously lacked them, has now become
broad practice (Brooker et al. 2009b; Brooker et al.
2010). Limited availability of spatial data, privacy
and confidentiality issues, restrictions to the access
and use of individual health incident and outcomes
data are some of the challenges that can be encoun-
tered particularly in working with human diseases.
Challenges with data ownership, the ability to link
publicly available data due to inconsi stencies in
data collection parameters and systems and limited
knowledge of the application and interpretation of
GIS in decision-making processes have also been
reported (McLafferty, 2003). Data ownership is
particularly problematic when it comes to access,
and laws vary widely in different countries.
Accessibility to data obtained via sponsors, donors
or grants may be restricted.
Donors should ensure that clear agreements are in
place to govern future ownership of the data col-
lected in their sponsored projects, and how the infra-
structure will be managed or integrated into local
systems once donor funding ceases. Donors and
local partners should ensure that clear guidelines
and standard operating procedures are established
around data entry (who, what, when and where)
and for the sharing of information between stake-
holders at the local level (Dunn et al. 1997; Sieber,
2000; Saikia, 2010). Some international organiza-
tions have already produced open access declarations
in support of publicly funded research been made
publicly available as a global public good (Chan
et al. 2005). This approach could be adopted by
other countries with the use of publicly funded
data. In the public health context, ethical issues
associated with rare conditions, confidentiality and
de-identification of data woul d need to be considered
and governed. In those cases, an ethics review
process would need to be developed to streamline
data access and use, and ensure that data are properly
de-identified.
The overarching rationale to make data available
and accessible is to ensure global public health is
protected. Geospatial tools are paramount to preven-
tion and containment of global threats. The inter-
national public health community therefore has
interest in ensuring that adequate regulations are in
place to govern the sharing of geospatial data for
public health purposes. This could be achieved
through existing international collaborative
approaches such as those discussed in the previous
section.
CONCLUDING REMARKS
The application of GIS technology in public
health and epidemiology is expanding, thanks to in-
creasing availability of the technology. The incorp-
oration of GIS technology into disease surveillance
systems and for the study of the distribution of
parasites and of their vectors has furthered our
understanding of the spatial components of disease
risk and distribution patterns. The application of
GIS technology to the study of parasitic diseases
has contributed significantly to the understanding
of parasite ecology and their associations with
disease distribut ion, enabling the development of
effective control and prevention interventions,
mainly in developing regions. However, it is
evident that GIS has been underutilized in some
areas of public health and in some regions. While
systemic limitations (lack of infrastructure, training,
long-term maintenance of database, uniform and
complete data collection, sharing of databases) may
have contributed to its underutilization, there are
several opportunities to improve free or low-cost
access to GIS infrastructure, develop local technical
capacity, and improve data availability and analysis
capacity. This can be achieved through well-
designed operational research and randomized
control trials that can provide adequate evidence
on the effectiveness of the GIS technology and
SDSSs particularly in areas where implementation
has so far been limited. Numerous lessons have
been learned from the application of GIS technology
in the developing world that can be translated to
other regions sharing similar public health chal-
lenges and risks, as well as for the understanding of
exotic diseases and risk factors among remote popu-
lations in industrialized regions. A good starting
point is to build local technical capacity in under-
resourced areas and to ensure that clear guidelines
are in place to facilitate the use of GIS infrastruc-
ture, and sharing and application of data to manage
public health problems. International collaborations
12S. M. Fletcher-Lartey and G. Caprarelli
that facilitate the sharing of knowledge and best
practice should be encouraged.
ACKNOWLEDGEMENTS
Two anonymous reviewers provided valuable comments
that led to an improved version of the manuscript.
FINANCIAL SUPPORT
This research received no specific grant from any funding
agency, commercial or not-for-profit sectors.
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