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HARMONIZE members visit the European Space Agency

NOV 13 2023

By HARMONIZE communication team

Earth Observation (EO) data generated by satellites are increasingly becoming useful for public health research. These high-resolution images and data streams are helping scientists gain unprecedented insights into health-related challenges, from predicting disease outbreaks to exploring the environmental determinants of human health. However, while the potential is great, harnessing this data can be challenging. Researchers need the right tools, infrastructure, and skills to access and handle this vast amount of information effectively.

One of the goals of the HARMONIZE project is to bridge the gap between satellite imagery and actionable insights for decision-makers in the public health sector. To contribute to this, the harmonizers from the Barcelona Supercomputing Center Prof Rachel Lowe and Dr Bruno Carvalho visited Φ-lab at the European Space Agency (ESA) in Frascati, Italy. Bruno and Rachel gave seminars about the HARMONIZE project and the multiple use cases of climate and environmental data in modelling and forecasting climate-sensitive infectious diseases.

Bruno stayed for a month as a visiting researcher at Φ-lab, hosted by Dr Rochelle Schneider, ESA’s Artificial Intelligence Applications Lead, who led a multi-award-winning ESA-UNICEF project on forecasting dengue outbreaks using a satellite-based ensemble machine learning approach. The overarching aim of the research visit was to understand the advantages, challenges, and limitations of using EO data for modelling infectious diseases. A key step in handling EO data for public health decision-making involves aggregating the pixel-based data from satellite images into administrative boundaries, such as municipalities or provinces. Within the context of the ESA-UNICEF dengue project, Dr Schneider's team developed a workflow to acquire and aggregate EO data to the federal units of Brazil, taking advantage of cloud computing open-sources for data processing and training/testing machine learning models. This approach alleviates concerns related to local computing power constraints and the burden of downloading massive spatiotemporal datasets, destined for subsequent reduction during the aggregation step.

The HARMONIZE project faces the challenging task of aggregating diverse datasets encompassing health, socioeconomic, climate, and EO data from multiple Latin American and Caribbean countries. Each data source presents their unique features and formats, making the process notably complex. Dedicated teams from the project consortium are crafting reproducible workflows tailored to harmonise this data. The research visit to the Φ-lab at ESA has contributed with invaluable insights that significantly enrich the development efforts of HARMONIZE. Moreover, this collaboration reinforces the synergy between experts from diverse disciplines, an essential way to improve decision-making and targeted interventions to control infectious diseases. As technology continues to evolve, the transformative potential of EO data in advancing public health research and fortifying global health becomes increasingly evident.