ÆSOP addresses the initial phases of an outbreak, trying to understand the complex forces operating in its initiation and spreading. Worldwide dissemination of an infectious disease requires a high transmission rate and an immunologically naïve population.ÆSOP will benefit from routinely collected data from distinct sources to increase the alert's predictive capability. We will treat the multimodal data with artificial intelligence and machine learning techniques for identifying potential groups of people or specific areas at risk. The use of continuously registered data from governmental services will lead to a low cost in feeding new data to the platform.The intended output is anticipating approximately one month of the currently available early alert approaches for outbreaks. Gaining one month ahead of current alert systems may be crucial to streamlining logistical issues for mitigating the impact and adopting containment measures for avoiding its spreading.

The Earth Observation Center (NOTE) at LAMCE uses its expertise in in-situ data collection, instrumentation and remote sensing to monitor physical, chemical and biological systems of the Earth, and promotes the full and open sharing of all its data to research communities, industry, academia, governments and civil society.The Earth Observation Center works to monitor and assess the status and changes in natural and artificial environments as a contribution to the establishment of international standards for measurements of the Earth's surface, atmosphere and ocean through its internal research and cooperation with partners, and to enable and disseminate data acquisition, access and storage.One of the goals of the Earth Observation Core is to contribute to the development of highly accurate twin or digital Earth models for monitoring and predicting environmental changes and their relation to human impact. The integration of in-situ, airborne and orbital data; high-performance computing and data science provides a unique source of data and information, enabling exploration of different scenarios and components of the Earth system, along with possible adaptation and mitigation strategies in association between local priorities and global challenges.

Doctoral thesis for the development of a methodology for the creation of "Integration and Analysis of Multisensor Satellite Data for the Development of Digital Twins for the Blue Economy" at the Laboratory of Computational Methods in Engineering (LAMCE) at COPPE/UFRJ. He currently works as a Research Scientist at LAMCE/COPPE/UFRJ in Remote Sensing of the Atmosphere and acquisition of environmental data, being responsible for the Environmental Satellite Data Dissemination Center.

Fabio Hochleitner holds a degree in Meteorology at the Institute of Geosciences (IGEO) and a Master's degree in Civil Engineering (Water Resources/Atmospheric Sciences) at the Alberto Luiz Coimbra Institute of Graduate Studies and Research in Engineering (COPPE), both at the Federal University of Rio de Janeiro (UFRJ). Doctoral thesis for the development of a methodology for the creation of "Integration and Analysis of Multisensor Satellite Data for the Development of Digital Twins for the Blue Economy" at the Laboratory of Computational Methods in Engineering (LAMCE) at COPPE/UFRJ. He served as Substitute Professor at the Meteorology Department teaching the disciplines of Numerical Methods and Computational Modeling. Founder of Aquamet Meteorologia, a technology-based company operating in the areas of Meteorology and Oceanography spin-off from LAMCE at the UFRJ Technological Park. He currently works as a Research Scientist at LAMCE/COPPE/UFRJ in Remote Sensing of the Atmosphere and acquisition of environmental data, being responsible for the Environmental Satellite Data Dissemination Center.Projects:- Centro de Excelência em Transformação Digital e Inteligência Artificial do Estado do Rio de Janeiro (Hub.Rio): Rede Temática em Energias Renováveis e Mudanças Climáticas- ARIEL – Autonomous Robot for Identification of Emulsified Liquids- ÆSOP EarlyAlert System of Outbreaks with Pandemic Potential- SPREME - SISTEMA DE PREVISÃO DE EVENTOS METEOROLÓGICOS EXTREMOS- Plataforma Integrada de Monitoramento e Observação Meteo Oceanográfica para a Baía de Guanabara- Plataforma Integrada de Monitoramento de Mobilidade Humana e Modelagem da Epidemia de COVID-19- Enabling Efficient Energy Usage with Data Mining and Integrated Demand- Modelagem e Simulação de Padrões de Mobilidade Humana com Aplicações ao Controle de Epidemias e Previsão de Fluxo Urbano- Projeto AZUL – Sistema de Observação Oceânica para a Bacia de Santos