What inspired your team to choose this challenge?

• Our team comes from Development countries where this tool can support people to take better decisions and prevent the spread of pandemics. Also want to put our expertise on data science for the human benefit.  

What was your approach to developing this project? 

•  Build a tool to help people to take better decisions during extraordinary events such as COVID19 pandemic. 

How did you use space agency data in your project? 

• We ingest the Global Imagery Browse Services (GIBS) APIs from NASA and combine it with other Health statistics and other external services, to provide users accurate information via a user-friendly interface.
  
  For our COVID19 Risk Level Score model; we extract from web services APIs variables such as:
  -Air Quality,
  -Air Temperature,
  -Humidity,
  -Pollution,
  -Carbon Monoxide,
  -GDP per capita,
  -Location,
  -Social Media trends,
  -Among others.
  
  Based on this vast amount of Data Sources connected through Application Programming Interfaces (APIs) web services we have the unique opportunity to measure, monitor and understand the social and economic impact of Governments decisions during Pandemic Times. The Geographical, Economical and Temporal data resources, provides our supervised machine learning model an exploratory analysis of Spatiotemporal dynamics of infectious diseases spread and it's socio-economic impact in Populations. (The Risk Score model is weighted by the values of the factors and the % of the population >= age 65.)  
  

What tools, coding languages, hardware, software did you use to develop your project? 

• NASA's Global Imagery Browse Services (GIBS) APIs
• COVID19 Open APIs
• Apache Spark 
• Casandra for Database
• Coding Languages: Python, C sharp and Javascript
• Power BI for Data Visualization

What problems and achievements did your team have?

• Our main challenge was to determine an accurate way on how to predict a spread rate of a virus/pandemic based on satellite imagery, health statistics and other external data, to provide a confidence score for end users. 
• We are proud of our team achievement of building a Live Demo 

Pitch: https://youtu.be/vCBrgkbCiNk
Slides: https://drive.google.com/file/d/1L-1Jx0CN42yFY82eHDZzifGAfNboXV9w/view?usp=sharing
Platform:http://covidatlas.co/
Monitor Demo: http://covidatlas.co/nasa.html
Dashboard Demo: http://covidatlas.co/dashboard.html

NASA's Global Imagery Browse Services (GIBS) APIs:
-GIBS APIs: https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+API+for+Developers#GIBSAPIforDevelopers-ImageryLayers&Endpoints
-GIBS Products: https://wiki.earthdata.nasa.gov/display/GIBS/GIBS+Available+Imagery+Products
-World Bank Data: https://data.worldbank.org/
-COVID19 API: https://covid19api.com/

-References:
(1) Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-ArtsDinh C. Nguyen, April 2020.
(2) Using online social networks to track a pandemic: A systematic review.
(3) Links between air pollution and COVID-19 in England. UK Medical Research Council, May, 2020.
(4) Incidence of COVID-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands. Bo Pieter Johannes Andree, May 2020.
(5) COVID-19 and air pollution: a deadly connection. World Economic Forum, April 2020.
(6) Propagation analysis and prediction of the COVID-19.  LixiangLia, March 2020.
(7) Modeling the spreading risk of 2019-nCoV. Johns Hopkins University Center for Systems Science and Engineering, February 20, 2020.

-Our team:
Cesar Méndez, Data Scientist living in Saudi Arabia.
Leo Camacho, Web Developer & APIs integrator from Costa Rica