Integration of environmental,
social and health data using GIS:
Lessons learned from three
disease outbreaks investigations
in rural areas
Christovam Barcellos
Walter Ramalho
Waneska Alves
Brazilian Health Ministry
• 3 Outbreak investigations
• Structure of Brazilian health
surveillance system
• Low cost alternatives for data
acquisition and analysis
Leishmaniosis outbreak
• Rural settlement since 1998
• 706 inhabitants
• Family farm
• 70 suspected cases during 2002
Outbreak dimensions
• Total population: 706
• Number of cases: 20
• Households with cases: 16
• Attack rate: 3%
Settlement characteristics
Subtropical climate
Recent provisonary settlement
Landless Workers' Movement (MST)
Close contact with animals
(dogs, chicken, pig)
Proximity to forest (rain forest and riparian vegetation)
(use of wood and hunting)
Mapping and questionnaire
Case location and habits characterization
Interactions people and environment
Pavlovski, 1939, theory of natural nidality
of transmissible diseases
GIS was employed to
• Characterize local landscape (RS)
• Measure distance between houses
and suspected risk sources
• Identify clusters of disease (spatial
Clusters of disease
Dual kernel rate
Primary layer:
households with cases
Secondary layer: All
Red – households with cases
Yellow – households without cases
Investigation participants and partners
Brazilian Health Ministry
Paraná State Health Secretary (SES)
Mariluz Municipal Health Secretary (SMS)
Research Institutes (Fiocruz, Brasilia
University, Maringá University)
Landless Workers' Movement (MST)
Waterborne Toxoplasmosis, Brazil
• Unusual acute toxoplasmosis
cases in an urban area
• Mapping the city water supply
• Residence location used as a
proxy of exposure
Waterborne Toxoplasmosis, Brazil
Moura et al. (2006) Emerging Infectious Diseases, CDC
Water reservoir contamination
by cat faeces
Santa Isabel do Ivai
138 (88%) of cases lived in the area served by reservoir A
and 17 individuals lived in area served by reservoir B
Hantavirosis transmission
foci identification Rio Grande do Sul
• Hantavirus Pulmonary Syndrome (HPS) is a disease
of increasing incidence in Rio Grande do Sul state
• The spatial distribution of cases is apparently
scattered in the state
• The aim of spatial analysis was to investigate the
role of agriculture activities and changing ecosystem
in the virus transmission
Case location > Transmission pattern identification > Preventive measures
Henkes, 2004
Hantavirosis transmission
foci identification Rio Grande do Sul
The majority of cases
occurred during spring, in
highland areas dominated
by secondary vegetation
and agricultural activity
An example of mapping
and deciding in a regional
Henkes, 2004
Information flux and
Health Surveillance Network
Ministry of Health
Epidemiological investigation and
technical support
State Health Secretary
Local Health Secretary
Other institutions
Data consolidation and analysis
Laboratory confirmation
Primary epidemiological investigation
Basic Health Care
Service (local)
Case diagnostic and notification
• Highly hierarchical (different roles in each level)
• Decentralized (present in all municipalities)
• Unequal (different capabilities and resources)
Health information systems
Live newborn Information system – SINASC
National Disease Notification System – SINAN
Mortality Information system – SIM
Hospital Information system – SIH
Plenty of data
But... Poor quality,
Incomplete coverage
Low capacity to analysis
National disease surveillance
Imediate notification of:
Suspected or confirmed case of: Botulism,
Carbuncle or Anthrax, Cholera, Yellow Fever,
West Nile Fever, Hantavirus, Human Influenza
by a new sub-type, Plague, Poliomyelitis,
Human Rabies, Measles, Acute
Icterohemorrhagic Fever, SARS, Smallpox and
Outbreak or clustering of cases or deaths by:
Unusual aggravations (unknown disease or
epidemiologic changes in known diseases),
Diphtheria, Acute Chagas Disease,
Meningococcal Disease
Epizootic and/or death of animals that could
precede the occurrence of diseases in humans:
Epizootic in non-human primates, other
epizootics of epidemiologic importance
GIS and RS demands for Public Health
Peopleware (Courses)
Software (Free and open)
Dataware (Health, population and
cartographical data)
Technological and methodological
RIPSA, 2003
Brazilian free (and user-friendly)
Reads images and
shapefiles, several spatial
analysis tools.
Calculates health
indicators and produces
simple thematic maps
Other available programs
Coordenadas do
centro do
Raio de
1 km
Available satellite images
Embrapa: Composed Landsat-TM bands 3, 4 and 5
INPE: Raw and classified CBERS images
A long and winding road…
Field work
Gathering data
Spatial analysis
The agricultural activities provide intensive
contact with the virus. The degradation of
naturally forested areas and the invasion of
intensive agriculture practices alter the
habitat of rodents, increasing food
availability due to grain storage.
• What kind of data we need? Where are them?
• Which objects must be mapped and how to georeference data?
• What kind of statistics do we use? Which software?
Free data and software
All data used are free and available
Decentralized and coordinated
Outbreaks are detected, investigated and
followed by local health authorities, supported by
a national task force and accompanied by NGO
Theory-driven investigations
Instead of technology-driven