@InProceedings{GuimarăesSousSantGome:2017:AnEsDo,
author = "Guimar{\~a}es, Ricardo Jos{\'e} de Paula Souza e and Sousa Neto,
Juliana Raiyanni and Santos, karla de Souza and Gomes, Alessandra
Rodrigues",
affiliation = "{} and {} and {} and {Instituto Nacional de Pesquisas Espaciais
(INPE)}",
title = "An{\'a}lise espacial da doen{\c{c}}a de Chagas no estado do
Par{\'a} no per{\'{\i}}odo de 2010-2014",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "3569--3575",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Chagas disease is a very large public health problem in the
Brazilian Amazon region. Transmission of vector-borne diseases is
often associated with changes in vegetation cover. Thus, the use
of geoprocessing is extremely important because it contributes to
the structuring and analysis of risk factors for the population.
The objective of this study was to evaluate the spatial behavior
of Chagas'' disease in the state of Par{\'a} in the period
2010-2014 using geoprocessing tools. Data from Chagas disease (CD)
were obtained from SINAN-NET. Other sources of data collection
were: IBGE, INPE and Google Earth Engine. The processing, analysis
and interpretation of data were performed in TerraView and ArcGis
software. In the spatial analysis were used the Global Moran
Index, Local Moran Index (LISA), Kernel density estimation and
Kernel ratio. The total of 9,737 cases of Chagas disease was
obtained from 82 municipalities of Par{\'a}. The correlation test
showed a positive correlation between the disease and the
population and a negative correlation between the disease and EVI
/ NDVI. The LISA allowed the identification of clusters of areas,
visualizing spatial dependence and analyzing spatial variability
behavior. The Kernel presented in all the studied years a cluster
of greater intensity in the municipality of Abaetetuba. The
results of the correlation analysis showed that constant
deforestation and population increase may be interfering with the
increase in CD cases. The Kernel analysis indicated the locations
for disease control and monitoring.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "60207",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSLT78",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLT78",
targetfile = "60207.pdf",
type = "Sa{\'u}de",
urlaccessdate = "28 abr. 2024"
}