César Capinha, Maurício Santos

Spatial-temporal modelling of Aedes aegypti (denver’s

mosquito) for a development of an early warning system in

Madeira Island

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

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

Aedes albopictus – Portugal Mainland and Europe

ECDC Projections

Ae. aegypti Observations - trap Locations

Spatial distribution of mosquito traps

155 traps: presences = 52 , absences n = 103 Period of observations: 2013-2016

Predictor variables

Spatial distribution of some predictor variables

Population density Land use

Houses density Mean temperature

Data Processing

Variables Extraction

Data Processing

Spatial Grid for prediction

Boosted Regression Trees

Modelling process

Potential spatial distribution of Aedes aegypti in Madeira Island

Results of the Spatial Model

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

Work in progress: spatial temporal modelling

Weekly Aedes aegypti mosquitoes by trap

time

Work in progress: spatial temporal modelling

What is the most appropriate

time lag?

Ae. aegypti, precipitation and

temperature weekly variation

Early warning signal for identification of high risk areas

Work in progress: spatial temporal modelling

Meteorological

conditionsReal-time GIS

simulationEarly warning

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.