Ovipositional Reproduction of the Dengue Vector for Identifying High-Risk Urban Areas

Author(s)

Lage, Mariana de Oliveira ; Barbosa, Gerson ; Andrade, Valmir ; Gomes, Henrique ; Chiaravalloti, Francisco ; Quintanilha, Jose Alberto

• Document type: Journal article
• Source: ECOHEALTH; v. 19, n. 1, p. 14-pg., 2022-04-19.

Abstract

Identification and classification of high-risk areas for the presence of Aedes aegypti is not an easy task. To develop suitable methods to identify this areas is an essential task that will increase the efficiency and effectiveness of control measures and to optimize the use of resources. The objectives of this study were to identify high- risk areas for the presence of Ae. aegypti using mosquito traps and household visits to identify breeding sites; to identify and validate aspects of the remote sensing images that could characterize these areas; to evaluate the relationship between this spatial risk classification and the occurrence of Ae. aegypti; and provide a methodology to the health and control vector services and prioritize these areas for development of control measure. Information about the geographical coordinates of these traps will enable us to apply the kriging spatial analysis tool to generate maps with the predicted numbers of Ae. aegypti. Satellite images were used to identify the characteristic features the four areas, so that other areas could also be classified using only the sensing remote images. The developed methodology enables the identification of high-risk areas for Ae. aegypti and for the occurrence of Dengue, as well as Zika fever and Chikungunya fever using only sensing remote images. These results allow health and vector control services to prioritize these areas for developing surveillance and control measures. The use of the available resources can be optimized and potentially promote a decrease in the expected incidences of these diseases, particularly Dengue. (AU)

• FAPESP's process: 15/06687-3 - Evaluation of importance from the strategic places in dispersion of the vector Aedes aegypti, and the use of premise condition index and remote sensing images for risk areas identification for Aedes aegypti
• Grantee: Gerson Laurindo Barbosa
• Support Opportunities: Regular Research Grants
• FAPESP's process: 20/01596-8 - Use of remote sensing and artificial intelligence to predict high risk areas for Aedes aegypti and Arbovirus infestation
• Grantee: Francisco Chiaravalloti Neto
• Support Opportunities: Regular Research Grants