This project will use a meteorological database made available by NASA and space agencies from other continents creating a data lake and developing predictive models with artificial intelligence for effectiveness in planning disease prevention actions and creating supply strategies, through integrated systems and APIs that provide climatic data, demographic density, temperature and crossing historical data using artificial intelligence to create a cloud dashboard to meet local and global needs.
The choice of the challenge relates to how the world was surprised by COVID-19. Information and data were not available and correlated to measure the severity and spread of contamination in the countries that are most exposed to polluting factors, climatic characteristics and demographic data. Our approach is to use NASA's map resources and real-time weather information to transform the data into strategic and public knowledge. The solution generates predictive analysis by correlating data through information from nitrogen dioxide maps, population density and impacted countries. The tools used were: Python (for artificial intelligence), Excel (scatter plots), G-suite, EARTH NOW (NASA), Google Data Studio and the Front Office for user interaction and the results generate accurate information about how impacts factors influence the spread of diseases.
Link for Demo Dashboard / Link for video Pitch
Python, Excel, Google Cloud, Earth data, Google data Studio and G-suíte