Since the COVID-19 pandemic began, there has been a proliferation of websites and portals of both scientific and non-scientific developed to share resources about the topics. These include resources hosted by the World Health Organization, U.S. National Institutes of Health/National Library of Medicine, U.S. Centers for Disease Control and Prevention (CDC), the U.S. Food and Drug Administration (FDA), and the U.S. Department of Agriculture (USDA).
Although these platforms present important information about COVID-19 and the global pandemic such as research updates but currently there is still a lack of unified virtual platforms and dashboards that integrate research findings about current environmental factors to the spread of COVID-19. As data are collected over time and space, they can be analyzed and compared across different geographic regions and climatic regimes, including the trend, patterns, correlation, causation and exploration of the potential impact of environmental factors and non-environmental factors on the spread of COVID-19.
Our solution is to use innovative ways to integrate real time data from all sources, primarily the text data of medical research and space-based Earth observation data to analyze the relationships between data points to understand the links between factors affecting COVID19.
Using advanced natural language processing technology (NLTK) and machine learning techniques (artificial intelligence) in the context of “big data analytics”, data points can be analyzed to understand both the spatial and temporal relationships between factors of interest. The relationship can be displayed in the simplest possible way in which it is not just comprehensible by trained scientists but also policy makers in general. As earth sciences and health sciences properties are important for policy makers to understand and to allocate resources, establishing potential links between human health and the environmental factors has important impacts on controlling the spread of COVID-19
The developed tools and technology could be use for better surveillance to connect COVID-19 cases such as raw data at local and regional levels with environmental factors and to determine if significant links exist. This could potentially lead to a new, innovative way to quickly identify COVID-19 cases using integrated data from global statistics, medical research, NASA data and other agencies
Using NLTK, machine learning frameworks, python, tableau, big data analytics techniques and advanced statistical methods, others
prototype in progress
WHO, 2020. World Health Organization.
U.S. National Institutes of Health/National Library of Medicine
U.S. Centers for Disease Control and Prevention
U.S. Food and Drug Administration
U.S. Department of Agriculture
NASA ESDS Cloud-Optimized Geotiffs (COGs)
Euro Data Cube SentinelHub resources
JAXA/ESA/NASA's data ingested in Euro Data Cube
NASA Giovanni
LANCE: NASA Near Real-Time Data and Imagery
Earthdata search
Earthdata Common Metadata Repository
NASA Earth Observatory
NASA Data Pathfinders
NASA COVID-19 data pathfinders
SEDAC Global COVID-19 Viewer
NASA open source software - NASA Github
NASA open source software - https://github.com/nasa
NASA's Black Marble
NOAA - Climate.gov
JAXA for Earth
JASMES Portal - Solar radiation reaching the earth's surface (photosynthetically available radiation), Cloudiness, Snow and sea ice cover, Dryness of vegetation (water stress trend), Soil moisture, Wildfire, Precipitation, Land and sea surface temperature, etc.
JAXA Himawari Monitor (P-Tree) - Sea Surface Temperature, Aerosol Optical Thickness, Radiation, Chlorophyll-a Concentration, Wild Fire, etc.