I am a Peruvian Environmental Engineer passionate about research on Landscape Ecology and Remote Sensing of the Environment. I am also interested to investigate the relationship between the environment and infectious diseases with innovative tools that lead us to improve life quality. I’m currently a student of Copernicus Master in Digital Earth, based in the University of Salzburg (Austria) and the University of South Britany (France).
I have experience in Remote Sensing projects using a variety of GIS and programming software such as QGIS, Google Earth Engine (GEE), R, and Python; and I’ve participated in projects related to remote sensing and infectious diseases such as: the identification of the main malaria vector breeding sites using drone multispectral imagery and Machine Learning algorithms, the assessment of Geographical accessibility to health facilities in Peru, and the use of time series analysis to predict dengue cases in the Peruvian Amazon using Environmental predictors derived from the ECMWF reanalysis, among others.
Download my resumé.
Acknowledgments
MSc Erasmus Mundus Copernicus Master in Digital Earth, 2021-2023
Paris Lodron Universität Salzburg, Université Bretagne Sud
BSc in Environmental Engineering, 2011-2016
Universidad Nacional Federico Villarreal, Perú
Land-use practices such as agriculture can impact mosquito vector breeding ecology, resulting in changes in disease transmission. The typical breeding habitats of Africa’s second most important malaria vector Anopheles funestus are large, semipermanent water bodies, which make them potential candidates for targeted larval source management. This is a technical workflow for the integration of drone surveys and mosquito larval sampling, designed for a case study aiming to characterise An. funestus breeding sites near two villages in an agricultural setting in Côte d’Ivoire. Using satellite remote sensing data, we developed an environmentally and spatially representative sampling frame and conducted paired mosquito larvae and drone mapping surveys from June to August 2021. To categorise the drone imagery, we also developed a land cover classification scheme with classes relative to An. funestus breeding ecology. We sampled 189 potential breeding habitats, of which 119 (63%) were positive for the Anopheles genus and nine (4.8%) were positive for An. funestus. We mapped 30.42 km2 of the region of interest including all water bodies which were sampled for larvae. These data can be used to inform targeted vector control efforts, although its generalisability over a large region is limited by the fine-scale nature of this study area. This paper develops protocols for integrating drone surveys and statistically rigorous entomological sampling, which can be adjusted to collect data on vector breeding habitats in other ecological contexts. Further research using data collected in this study can enable the development of deep-learning algorithms for identifying An. funestus breeding habitats across rural agricultural landscapes in Côte d’Ivoire and the analysis of risk factors for these sites.
Interest in larval source management (LSM) as an adjunct intervention to control and eliminate malaria transmission has recently increased mainly because long-lasting insecticidal nets (LLINs) and indoor residual spray (IRS) are ineffective against exophagic and exophilic mosquitoes. In Amazonian Peru, the identification of the most productive, positive water bodies would increase the impact of targeted mosquito control on aquatic life stages. The present study explores the use of unmanned aerial vehicles (drones) for identifying Nyssorhynchus darlingi (formerly Anopheles darlingi) breeding sites with high-resolution imagery (~0.02m/pixel) and their multispectral profile in Amazonian Peru. Our results show that high-resolution multispectral imagery can discriminate a profile of water bodies where Ny. darlingi is most likely to breed (overall accuracy 86.73%- 96.98%) with a moderate differentiation of spectral bands. This work provides proof-of-concept of the use of high-resolution images to detect malaria vector breeding sites in Amazonian Peru and such innovative methodology could be crucial for LSM malaria integrated interventions.