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César Capinha, Maurício Santos
Spatial-temporal modelling of Aedes aegypti (denver’s
mosquito) for a development of an early warning system in
Madeira Island
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Presentation contents
1. Introduction
1.1. Mosquito Aedes aegypti in Madeira Island, a briefly context
2. A Spatial modelling of Aedes aegypti
2.1. Aedes aegypti observations: traps locations
2.2. Predictor variables
2.3. Data processing
3. Results of spatial prediction and variables importance
4. Work in progress: spatial-temporal modelling for an early warning
system
5. Final remarks
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Aedes aegypti in Madeira Island, a briefly context
2005Observed for
the firs time
2012-2013Denv
outbreak
2187Total probable
dengue cases
80Casos
exportados
Madeira Island Location
First observation detected Observations during the last years
DengueZikaYellow fever
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Aedes albopictus – Portugal Mainland and Europe
ECDC Projections
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Ae. aegypti Observations - trap Locations
Spatial distribution of mosquito traps
155 traps: presences = 52 , absences n = 103 Period of observations: 2013-2016
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Predictor variables
Spatial distribution of some predictor variables
Population density Land use
Houses density Mean temperature
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Data Processing
Variables Extraction
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Data Processing
Spatial Grid for prediction
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Boosted Regression Trees
Modelling process
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Potential spatial distribution of Aedes aegypti in Madeira Island
Results of the Spatial Model
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Results of Spatial Model
Variables importance
Houses density
Mean temperature
Mixed articial territories
Public spaces
Green areas
Forests and natural spaces
Agricultural areas
Banana trees
Parks and public gardens
Descontinuos urban spaces
Water bodies
others
Population density
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Work in progress: spatial temporal modelling
Weekly Aedes aegypti mosquitoes by trap
time
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Work in progress: spatial temporal modelling
What is the most appropriate
time lag?
Ae. aegypti, precipitation and
temperature weekly variation
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Early warning signal for identification of high risk areas
Work in progress: spatial temporal modelling
Meteorological
conditionsReal-time GIS
simulationEarly warning
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Final Remarks
• The spatial prediction showed a higher suitability.
• The higher importance of socio-environmental variables.
• Spatial modelling at intra-urban scale for further refining our spatialmodel.
• Spatial-temporal modelling and early warning system: thechallenges.
• The real application of the early warning system as tool for betterprevention and mitigation of related mosquito-transmitteddiseases.