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Using remote sensing for risk mapping of adult Aedes Aegypti infestation

Grant number: 19/08205-7
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): July 31, 2019
Effective date (End): October 30, 2019
Field of knowledge:Health Sciences - Collective Health - Public Health
Principal Investigator:Francisco Chiaravalloti Neto
Grantee:Camila Lorenz
Supervisor abroad: Marcia Caldas de Castro
Home Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Local de pesquisa : Harvard University, Cambridge, United States  
Associated to the scholarship:17/10297-1 - Identification of risk areas for arboviruses using traps for adults of Aedes Aegypti and Aedes albopictus and remote sensing images, BP.PD


Mosquito-borne disease affects million people in the world, and its transmission area continues to expand due to many factors linked to urban sprawl, increased travel and global warming. The Aedes aegypti mosquito plays a central role in the dissemination of dengue, zika, chikungunya and urban yellow fever. Current preventative measures include mosquito control programs but unfortunately, identifying the mosquito habitats over a large geographic area based only on field survey is time-consuming and labour intensive. Recently, several studies in the literature demonstrate the utility of remote sensing technology in the risk assessment of vector-borne diseases and vector population. Thus, the objective of this internship is to assess the potential of satellite images, Landsat TM 8 and Sentinel Copernicus 3, for identifying adult habitats of Ae aegypti vector and for determining land features associated with its infestation index in São José do Rio Preto/SP, Brazil. To describe the infestation of Ae. aegypti throughout study area, we will use the kriging method and data from 60 mosquito traps; the remote sensing images will be classified for landcover types using a supervised classification method; and surface temperature and vegetation maps will be construct using vector data. Multivariate cluster analysis will be used to examine the associations between the Ae. aegypti infestation and the land use/cover and temperature. It will be possible to elaborate a model to predict the areas with most suitable habitats for these mosquitoes, helping and optimizing the surveillance and control measures.