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Earth Observation and Artificial Intelligence for Global Health

The Health sector is by nature complex, interdisciplinary and multi-sectorial, offering a wealth of opportunities for the exploitation of Earth Observation data through the application of Artificial Intelligence tools and innovative modelling techniques. In this context, ESA is actively seeking collaborations with public and private actors and non-governmental organisations, to co-design new solutions that could improve the effectiveness and efficiency of healthcare policies and operations, creating more resilient societies” according to Dr. Stefano Ferretti from the Earth Observation Programme Directorate of ESA.

Within the Science, Applications and Climate Department of EOP, one such project has been recently completed thanks to the innovative work carried out by industry partners together with the public health authorities of Italy. The project addressed the West Nile Virus (WNV), which is a mosquito-borne disease commonly transmitted to humans through the bite of an infected mosquito. Its consequences can be very serious for human health, causing in 2% of cases severe damage to the nervous system or to other organs, and even death. Furthermore, affecting animals, especially horses, the infection can lead to economic impacts in some areas of the planet. In this context, many resources are spent to tackle this problem which is now a global issue.

In order for the WNV to spread, certain climatic and environmental conditions are required. The cycle begins in rural areas, where there is a strong presence of stagnant water that allows the mosquitoes to reproduce and migratory birds to feed. Surveillance activities in the field are extremely demanding, both from an economic perspective and from a human resources point of view. Knowing where to look is therefore essential to intervene promptly before the virus spreads, mobilising all the necessary structures (laboratories, veterinarians, medical teams, etc.) efficiently.

The Earth Observation data obtainable through ESA’s Copernicus constellation is very helpful to understand if the ideal climatic and environmental conditions for mosquito population growth, and hence the spread of the virus, are present (e.g. water presence, soil moisture levels, vegetation density, suitable temperatures).

Some key environmental variables (geographical, climatological and hydrological) that influence the transmission cycle of the virus can be monitored efficiently from satellites, which are capable of capturing these parameters frequently on a global scale. Annamaria Conte, Head of the Statistics and GIS Unit at Istituto Zooprofilattico dell’Abruzzo e del Molise “G. Caporale” in Teramo, Italy, states: “The increasing availability and complexity of EO data has led to new opportunities and challenges in human and veterinary epidemiology. The main research goal is to generate new knowledge and the greatest challenge in the next decade will be to move from big data availability towards creating innovative value-added services”.

Moreover, such environamental data can be integrated with Artificial Intelligence to create more reliable prediction models. For example, the AIDEO project (“Artificial Intelligence and Earth Observation Data: innovative methods for monitoring West Nile Disease (WND) spread in Italy”) provides a model to predict where and when the WND could spread in Italy. According to Simone Calderara, Professor at the AImageLab research group of the University of Modena and Reggio Emilia, Italy: “with AI and modern machine learning solutions it is possible to extract knowledge from the vast amount of data from EO missions and exploit the richness of weak correlations and recurrent patterns hidden inside. The unsupervised deep knowledge extraction module from AIDEO can be the cornerstone of several different applications that can benefit from the positive transfer of knowledge from the ESA Copernicus data stream”.

The possible applications of Artificial Intelligence and Big Data analytics techniques range across many sectors: from climate change monitoring to energy and infrastructures management, from environmental protection to health.

Public entities and private companies will be able to exploit this data to know specifically where to intervene to prevent the spread of this type of disease and others.

Earlier this year, the ESA Earth Observation Programme organized the “EO and AI for Health and Urban Resilience” workshop.

Building on its outcomes, ESA is currently planning innovative projects in the  wider health resilience domain, seeking in-situ data providers and big data analytics solutions, creating a solid framework for collaboration. If you are interested in health-related topics or if you have a specific use case to propose, please get in touch with us.

For more information, please consult the article “Vector-borne disease circulation predicted with EO data help”, available on the EO4society website.

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